Plot

EDA Plot for each crop data

plot_fao_data(data_fao)

## [1] "Adding columns for year and week"
## [1] "Adding columns for year and month"

linear reg for yield VS EHF 95

Abbotsford weekly

## [1] "Results for crop: Apples"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -27837  -6474  -1373   4047  41932 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 247624.160  53497.038   4.629  0.00169 **
## Week_1       -2171.531   3826.707  -0.567  0.58596   
## Week_2       -2744.458   2644.290  -1.038  0.32968   
## Week_3        3641.847   3466.401   1.051  0.32413   
## Week_4        3003.179   6048.615   0.497  0.63289   
## Week_5        1178.247   3445.783   0.342  0.74121   
## Week_6       -8750.445   4915.786  -1.780  0.11294   
## Week_7        4885.760   6409.091   0.762  0.46775   
## Week_8        5235.628   4971.863   1.053  0.32308   
## Week_9        1747.502   4600.510   0.380  0.71394   
## Week_10      -3484.455   4713.238  -0.739  0.48086   
## Week_11      -7730.497   6721.097  -1.150  0.28329   
## Week_12      -5157.226   4302.043  -1.199  0.26491   
## Week_13       3554.688   4929.279   0.721  0.49136   
## Week_14       2938.131  10286.955   0.286  0.78243   
## Week_15      11179.958   7376.340   1.516  0.16808   
## Week_16          1.334   3193.296   0.000  0.99968   
## Week_17      -3851.450   4653.844  -0.828  0.43190   
## Week_18       4466.951   3566.116   1.253  0.24572   
## Week_19       3144.828   2232.205   1.409  0.19654   
## Week_20       7169.321   4975.559   1.441  0.18758   
## Week_21      -1530.657   4360.146  -0.351  0.73461   
## Week_22      -3232.952   2740.419  -1.180  0.27200   
## Week_23      -2143.645   4251.514  -0.504  0.62771   
## Week_24       5438.291   4307.202   1.263  0.24229   
## Week_25      -7676.930   5358.130  -1.433  0.18982   
## Week_26        777.006   1478.901   0.525  0.61356   
## Week_27       7691.774   4315.853   1.782  0.11257   
## Week_28       3580.131   4283.071   0.836  0.42748   
## Week_29      -1154.881   3714.927  -0.311  0.76384   
## Week_30       2083.311   2774.364   0.751  0.47421   
## Week_31       5982.369  12198.921   0.490  0.63702   
## Week_32      -5687.078   3614.449  -1.573  0.15427   
## Week_33      -8240.645   5629.085  -1.464  0.18137   
## Week_34      27195.753   9710.787   2.801  0.02318 * 
## Week_35      -8417.524   5877.329  -1.432  0.18998   
## Week_36       8261.557   6418.785   1.287  0.23405   
## Week_37      -1111.867  10587.008  -0.105  0.91894   
## Week_38      -3434.407   6234.828  -0.551  0.59678   
## Week_39       -773.375   5236.137  -0.148  0.88623   
## Week_40      -6421.768   8470.863  -0.758  0.47013   
## Week_41      -8203.312   7828.657  -1.048  0.32533   
## Week_42      11641.966   6518.586   1.786  0.11193   
## Week_43     -12452.499   8075.365  -1.542  0.16164   
## Week_44       2689.081   6051.644   0.444  0.66856   
## Week_45     -14715.632   9651.161  -1.525  0.16583   
## Week_46      16405.115   9062.185   1.810  0.10784   
## Week_47      -4234.774   4100.397  -1.033  0.33192   
## Week_48       2209.198   5307.865   0.416  0.68820   
## Week_49       6830.131   5089.663   1.342  0.21645   
## Week_50      -7680.498   5335.802  -1.439  0.18799   
## Week_51      -3394.036   3890.915  -0.872  0.40846   
## Week_52       2109.363   6764.758   0.312  0.76315   
## Week_53      -2308.067   4283.919  -0.539  0.60471   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36040 on 8 degrees of freedom
## Multiple R-squared:  0.9491, Adjusted R-squared:  0.6122 
## F-statistic: 2.817 on 53 and 8 DF,  p-value: 0.06071

## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -4213  -1125    -84   1147   4983 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 41267.00    7396.44   5.579 0.000523 ***
## Week_1        159.50     529.08   0.301 0.770741    
## Week_2       -459.92     365.60  -1.258 0.243872    
## Week_3        331.46     479.26   0.692 0.508751    
## Week_4        490.41     836.27   0.586 0.573763    
## Week_5        -86.59     476.41  -0.182 0.860296    
## Week_6      -1051.44     679.65  -1.547 0.160443    
## Week_7        165.41     886.11   0.187 0.856567    
## Week_8       1309.93     687.40   1.906 0.093157 .  
## Week_9       -566.49     636.06  -0.891 0.399123    
## Week_10       -55.58     651.65  -0.085 0.934121    
## Week_11      -710.73     929.25  -0.765 0.466328    
## Week_12      -301.10     594.79  -0.506 0.626357    
## Week_13        88.47     681.52   0.130 0.899920    
## Week_14       942.56    1422.26   0.663 0.526139    
## Week_15       838.48    1019.84   0.822 0.434803    
## Week_16       -32.46     441.50  -0.074 0.943191    
## Week_17      -828.63     643.43  -1.288 0.233808    
## Week_18       392.15     493.05   0.795 0.449360    
## Week_19       690.71     308.62   2.238 0.055595 .  
## Week_20       718.53     687.91   1.045 0.326784    
## Week_21       112.44     602.83   0.187 0.856675    
## Week_22       -14.73     378.89  -0.039 0.969941    
## Week_23      -736.03     587.81  -1.252 0.245879    
## Week_24       221.83     595.51   0.373 0.719193    
## Week_25      -305.94     740.81  -0.413 0.690462    
## Week_26      -271.02     204.47  -1.325 0.221611    
## Week_27      1663.55     596.71   2.788 0.023635 *  
## Week_28      -404.66     592.17  -0.683 0.513685    
## Week_29       108.40     513.62   0.211 0.838130    
## Week_30       388.34     383.58   1.012 0.340983    
## Week_31      -464.93    1686.61  -0.276 0.789797    
## Week_32        -4.64     499.73  -0.009 0.992819    
## Week_33     -1009.16     778.27  -1.297 0.230892    
## Week_34      2567.53    1342.60   1.912 0.092195 .  
## Week_35     -1308.31     812.59  -1.610 0.146055    
## Week_36      1224.31     887.45   1.380 0.205048    
## Week_37      -470.14    1463.75  -0.321 0.756299    
## Week_38        77.30     862.02   0.090 0.930749    
## Week_39        24.27     723.94   0.034 0.974080    
## Week_40      -432.06    1171.17  -0.369 0.721764    
## Week_41       473.49    1082.38   0.437 0.673353    
## Week_42      1507.43     901.25   1.673 0.132946    
## Week_43      -210.32    1116.49  -0.188 0.855269    
## Week_44      -702.37     836.69  -0.839 0.425586    
## Week_45      -764.99    1334.36  -0.573 0.582193    
## Week_46      1168.18    1252.93   0.932 0.378430    
## Week_47      -548.78     566.92  -0.968 0.361390    
## Week_48       768.53     733.86   1.047 0.325590    
## Week_49       169.41     703.69   0.241 0.815807    
## Week_50      -489.42     737.72  -0.663 0.525710    
## Week_51      -770.43     537.95  -1.432 0.189992    
## Week_52       234.75     935.29   0.251 0.808147    
## Week_53      -289.59     592.29  -0.489 0.638006    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4983 on 8 degrees of freedom
## Multiple R-squared:  0.9163, Adjusted R-squared:  0.3614 
## F-statistic: 1.651 on 53 and 8 DF,  p-value: 0.231

## [1] "Results for crop: Maize (corn)"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -5994  -1963   -161   1272  10520 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 100208.28   12862.34   7.791 5.28e-05 ***
## Week_1          70.66     920.06   0.077   0.9407    
## Week_2        -280.53     635.77  -0.441   0.6707    
## Week_3        1157.06     833.43   1.388   0.2025    
## Week_4        -113.07    1454.27  -0.078   0.9399    
## Week_5        -179.41     828.47  -0.217   0.8340    
## Week_6       -1901.37    1181.91  -1.609   0.1463    
## Week_7         878.86    1540.94   0.570   0.5841    
## Week_8        1843.94    1195.39   1.543   0.1615    
## Week_9       -1332.39    1106.10  -1.205   0.2628    
## Week_10       1049.61    1133.21   0.926   0.3814    
## Week_11      -2306.89    1615.96  -1.428   0.1913    
## Week_12       -948.67    1034.34  -0.917   0.3859    
## Week_13        697.73    1185.15   0.589   0.5723    
## Week_14       1442.84    2473.30   0.583   0.5757    
## Week_15       1522.11    1773.50   0.858   0.4157    
## Week_16        789.24     767.77   1.028   0.3340    
## Week_17      -1225.56    1118.93  -1.095   0.3053    
## Week_18       1141.85     857.40   1.332   0.2196    
## Week_19        993.73     536.69   1.852   0.1012    
## Week_20       1066.51    1196.28   0.892   0.3987    
## Week_21       -839.57    1048.31  -0.801   0.4463    
## Week_22       -927.81     658.88  -1.408   0.1967    
## Week_23      -1178.51    1022.20  -1.153   0.2822    
## Week_24       1360.14    1035.58   1.313   0.2255    
## Week_25      -2343.97    1288.26  -1.819   0.1063    
## Week_26        269.62     355.57   0.758   0.4700    
## Week_27       3366.10    1037.66   3.244   0.0118 *  
## Week_28       -224.66    1029.78  -0.218   0.8328    
## Week_29       1036.99     893.18   1.161   0.2791    
## Week_30        463.16     667.04   0.694   0.5071    
## Week_31       3845.57    2933.00   1.311   0.2262    
## Week_32      -1285.01     869.03  -1.479   0.1775    
## Week_33       -673.49    1353.41  -0.498   0.6321    
## Week_34       6794.01    2334.77   2.910   0.0196 *  
## Week_35      -2673.52    1413.09  -1.892   0.0951 .  
## Week_36       4127.59    1543.27   2.675   0.0282 *  
## Week_37      -2474.94    2545.44  -0.972   0.3594    
## Week_38       -349.25    1499.05  -0.233   0.8216    
## Week_39       -695.63    1258.93  -0.553   0.5957    
## Week_40      -3333.10    2036.66  -1.637   0.1404    
## Week_41       -613.71    1882.25  -0.326   0.7528    
## Week_42       3909.47    1567.27   2.494   0.0373 *  
## Week_43      -3417.51    1941.57  -1.760   0.1164    
## Week_44        315.47    1455.00   0.217   0.8338    
## Week_45      -4087.96    2320.44  -1.762   0.1161    
## Week_46       4392.16    2178.83   2.016   0.0786 .  
## Week_47       -625.61     985.86  -0.635   0.5434    
## Week_48        956.91    1276.17   0.750   0.4748    
## Week_49       1533.99    1223.71   1.254   0.2454    
## Week_50      -1444.04    1282.89  -1.126   0.2930    
## Week_51      -1701.51     935.50  -1.819   0.1064    
## Week_52       1976.93    1626.46   1.215   0.2588    
## Week_53      -1614.43    1029.99  -1.567   0.1557    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8666 on 8 degrees of freedom
## Multiple R-squared:  0.9698, Adjusted R-squared:  0.7694 
## F-statistic: 4.841 on 53 and 8 DF,  p-value: 0.01152

## [1] "Results for crop: Peaches and nectarines"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5837.8 -1725.3  -198.2  1208.5  7536.9 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 123407.92   10478.35  11.777 2.47e-06 ***
## Week_1         806.42     749.53   1.076   0.3133    
## Week_2         -46.83     517.93  -0.090   0.9302    
## Week_3         899.86     678.96   1.325   0.2216    
## Week_4       -1270.65    1184.73  -1.073   0.3148    
## Week_5         -69.53     674.92  -0.103   0.9205    
## Week_6          86.49     962.84   0.090   0.9306    
## Week_7        -317.43    1255.33  -0.253   0.8068    
## Week_8        -238.38     973.83  -0.245   0.8128    
## Week_9        -164.69     901.09  -0.183   0.8595    
## Week_10        202.39     923.17   0.219   0.8320    
## Week_11       -500.89    1316.45  -0.380   0.7135    
## Week_12       -479.77     842.63  -0.569   0.5847    
## Week_13       -292.09     965.49  -0.303   0.7700    
## Week_14       2049.21    2014.88   1.017   0.3389    
## Week_15       1394.37    1444.79   0.965   0.3628    
## Week_16         81.82     625.46   0.131   0.8991    
## Week_17         44.46     911.54   0.049   0.9623    
## Week_18        -78.92     698.49  -0.113   0.9128    
## Week_19        394.91     437.22   0.903   0.3928    
## Week_20        535.20     974.55   0.549   0.5979    
## Week_21       -553.01     854.01  -0.648   0.5354    
## Week_22       -300.79     536.76  -0.560   0.5906    
## Week_23       -437.69     832.73  -0.526   0.6134    
## Week_24        771.14     843.64   0.914   0.3874    
## Week_25      -1660.72    1049.49  -1.582   0.1522    
## Week_26       -132.08     289.67  -0.456   0.6605    
## Week_27       1726.28     845.34   2.042   0.0754 .  
## Week_28         67.73     838.92   0.081   0.9376    
## Week_29        495.58     727.63   0.681   0.5150    
## Week_30        278.22     543.41   0.512   0.6225    
## Week_31       3385.97    2389.38   1.417   0.1942    
## Week_32        212.87     707.95   0.301   0.7713    
## Week_33      -1152.92    1102.56  -1.046   0.3263    
## Week_34       1066.17    1902.03   0.561   0.5905    
## Week_35        624.93    1151.18   0.543   0.6020    
## Week_36      -2375.88    1257.23  -1.890   0.0955 .  
## Week_37      -1847.96    2073.65  -0.891   0.3989    
## Week_38        649.91    1221.20   0.532   0.6091    
## Week_39         88.93    1025.59   0.087   0.9330    
## Week_40      -1053.59    1659.17  -0.635   0.5432    
## Week_41        804.23    1533.38   0.524   0.6142    
## Week_42       1521.51    1276.78   1.192   0.2675    
## Week_43         42.96    1581.70   0.027   0.9790    
## Week_44      -1031.52    1185.32  -0.870   0.4095    
## Week_45      -2714.56    1890.35  -1.436   0.1889    
## Week_46       3581.81    1774.99   2.018   0.0783 .  
## Week_47        268.70     803.14   0.335   0.7466    
## Week_48       1108.38    1039.64   1.066   0.3175    
## Week_49        443.45     996.90   0.445   0.6682    
## Week_50       -952.43    1045.11  -0.911   0.3888    
## Week_51       -326.53     762.11  -0.428   0.6796    
## Week_52        324.71    1325.00   0.245   0.8126    
## Week_53        -68.02     839.08  -0.081   0.9374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7060 on 8 degrees of freedom
## Multiple R-squared:  0.9275, Adjusted R-squared:  0.4472 
## F-statistic: 1.931 on 53 and 8 DF,  p-value: 0.1623

## [1] "Results for crop: Wheat"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3765.4  -913.4  -359.8   954.4  4594.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 36184.14    6340.82   5.707 0.000451 ***
## Week_1        421.32     453.57   0.929 0.380112    
## Week_2       -406.31     313.42  -1.296 0.230984    
## Week_3        549.88     410.86   1.338 0.217566    
## Week_4         89.98     716.92   0.126 0.903221    
## Week_5        -56.65     408.42  -0.139 0.893107    
## Week_6      -1020.79     582.65  -1.752 0.117878    
## Week_7       -230.95     759.65  -0.304 0.768870    
## Week_8       1753.05     589.30   2.975 0.017740 *  
## Week_9       -853.03     545.28  -1.564 0.156359    
## Week_10       127.30     558.64   0.228 0.825462    
## Week_11     -1291.86     796.63  -1.622 0.143535    
## Week_12      -263.73     509.91  -0.517 0.618997    
## Week_13       251.56     584.25   0.431 0.678142    
## Week_14      1259.70    1219.28   1.033 0.331753    
## Week_15       477.74     874.29   0.546 0.599675    
## Week_16       -45.16     378.49  -0.119 0.907962    
## Week_17      -935.08     551.60  -1.695 0.128482    
## Week_18       539.98     422.68   1.278 0.237243    
## Week_19       531.49     264.58   2.009 0.079419 .  
## Week_20       759.79     589.74   1.288 0.233632    
## Week_21       -44.56     516.79  -0.086 0.933410    
## Week_22       -95.45     324.81  -0.294 0.776346    
## Week_23      -660.23     503.92  -1.310 0.226494    
## Week_24        94.10     510.52   0.184 0.858345    
## Week_25      -237.42     635.08  -0.374 0.718232    
## Week_26      -186.85     175.29  -1.066 0.317553    
## Week_27      1762.13     511.54   3.445 0.008762 ** 
## Week_28      -681.49     507.66  -1.342 0.216306    
## Week_29      -174.30     440.32  -0.396 0.702555    
## Week_30       302.91     328.84   0.921 0.383902    
## Week_31       918.94    1445.90   0.636 0.542818    
## Week_32       -66.10     428.41  -0.154 0.881195    
## Week_33      -718.92     667.20  -1.078 0.312663    
## Week_34      2947.85    1150.99   2.561 0.033587 *  
## Week_35     -1253.79     696.62  -1.800 0.109583    
## Week_36      1010.79     760.80   1.329 0.220628    
## Week_37      -676.69    1254.84  -0.539 0.604384    
## Week_38        86.66     738.99   0.117 0.909536    
## Week_39       125.72     620.62   0.203 0.844523    
## Week_40     -1368.57    1004.02  -1.363 0.209980    
## Week_41       722.08     927.90   0.778 0.458852    
## Week_42      1921.01     772.63   2.486 0.037737 *  
## Week_43      -572.65     957.14  -0.598 0.566194    
## Week_44      -751.93     717.28  -1.048 0.325130    
## Week_45      -841.69    1143.92  -0.736 0.482867    
## Week_46      1151.20    1074.11   1.072 0.315088    
## Week_47      -462.00     486.01  -0.951 0.369636    
## Week_48       592.93     629.12   0.942 0.373537    
## Week_49       263.89     603.26   0.437 0.673364    
## Week_50      -398.73     632.43  -0.630 0.545969    
## Week_51      -874.17     461.18  -1.896 0.094619 .  
## Week_52       585.33     801.80   0.730 0.486204    
## Week_53      -435.37     507.76  -0.857 0.416150    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4272 on 8 degrees of freedom
## Multiple R-squared:  0.9421, Adjusted R-squared:  0.5586 
## F-statistic: 2.456 on 53 and 8 DF,  p-value: 0.08845

## [1] "Results for crop: Wine"
## [1] "No data available for this crop."
## [1] "Results for crop: Blueberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3404.7 -1029.7  -210.9   722.1  4629.5 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 23961.701   6607.473   3.626  0.00672 **
## Week_1       -702.709    472.641  -1.487  0.17539   
## Week_2        157.816    326.599   0.483  0.64189   
## Week_3       -292.068    428.139  -0.682  0.51439   
## Week_4       1208.512    747.071   1.618  0.14440   
## Week_5      -1049.263    425.592  -2.465  0.03899 * 
## Week_6       -630.246    607.154  -1.038  0.32961   
## Week_7        751.474    791.593   0.949  0.37025   
## Week_8       1135.322    614.080   1.849  0.10166   
## Week_9       -500.700    568.214  -0.881  0.40392   
## Week_10      -701.512    582.137  -1.205  0.26261   
## Week_11     -1225.466    830.130  -1.476  0.17812   
## Week_12      -449.506    531.350  -0.846  0.42215   
## Week_13       933.924    608.820   1.534  0.16358   
## Week_14      -633.648   1270.552  -0.499  0.63140   
## Week_15      1312.632    911.059   1.441  0.18762   
## Week_16       652.696    394.407   1.655  0.13654   
## Week_17      -634.967    574.801  -1.105  0.30142   
## Week_18      -487.901    440.455  -1.108  0.30017   
## Week_19       232.146    275.702   0.842  0.42423   
## Week_20      1266.244    614.536   2.060  0.07330 . 
## Week_21      -303.061    538.526  -0.563  0.58902   
## Week_22     -1094.305    338.472  -3.233  0.01200 * 
## Week_23       661.858    525.109   1.260  0.24304   
## Week_24        57.603    531.987   0.108  0.91644   
## Week_25        -4.102    661.788  -0.006  0.99521   
## Week_26       -61.315    182.661  -0.336  0.74575   
## Week_27       463.250    533.055   0.869  0.41014   
## Week_28       772.263    529.007   1.460  0.18246   
## Week_29      -846.214    458.834  -1.844  0.10237   
## Week_30       -29.721    342.664  -0.087  0.93301   
## Week_31      1311.893   1506.701   0.871  0.40928   
## Week_32       -88.776    446.424  -0.199  0.84733   
## Week_33     -1191.185    695.254  -1.713  0.12501   
## Week_34      1181.500   1199.389   0.985  0.35343   
## Week_35       366.011    725.915   0.504  0.62771   
## Week_36      -315.120    792.791  -0.397  0.70141   
## Week_37       915.710   1307.612   0.700  0.50360   
## Week_38     -1073.071    770.070  -1.393  0.20097   
## Week_39      -931.650    646.721  -1.441  0.18767   
## Week_40       377.127   1046.245   0.360  0.72784   
## Week_41      -810.362    966.925  -0.838  0.42631   
## Week_42       528.123    805.117   0.656  0.53026   
## Week_43     -1214.846    997.397  -1.218  0.25792   
## Week_44      1381.988    747.445   1.849  0.10164   
## Week_45     -1594.716   1192.025  -1.338  0.21774   
## Week_46      1576.992   1119.280   1.409  0.19652   
## Week_47       438.031    506.444   0.865  0.41227   
## Week_48      -479.170    655.580  -0.731  0.48569   
## Week_49        99.753    628.629   0.159  0.87785   
## Week_50      -512.787    659.030  -0.778  0.45891   
## Week_51         9.542    480.571   0.020  0.98464   
## Week_52      -301.734    835.522  -0.361  0.72735   
## Week_53      -197.042    529.111  -0.372  0.71927   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4452 on 8 degrees of freedom
## Multiple R-squared:  0.9341, Adjusted R-squared:  0.4972 
## F-statistic: 2.138 on 53 and 8 DF,  p-value: 0.1266

## [1] "Results for crop: Grapes"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10582.2  -2734.3   -964.6   2582.2  12444.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  86931.8    19493.7   4.459  0.00211 **
## Week_1         622.7     1394.4   0.447  0.66705   
## Week_2        -813.6      963.6  -0.844  0.42301   
## Week_3       -1019.3     1263.1  -0.807  0.44301   
## Week_4        1161.6     2204.0   0.527  0.61246   
## Week_5         974.9     1255.6   0.776  0.45985   
## Week_6       -3132.8     1791.3  -1.749  0.11842   
## Week_7        2375.3     2335.4   1.017  0.33888   
## Week_8        1280.3     1811.7   0.707  0.49982   
## Week_9       -1105.3     1676.4  -0.659  0.52820   
## Week_10       -830.9     1717.5  -0.484  0.64148   
## Week_11       -555.4     2449.1  -0.227  0.82630   
## Week_12       -127.8     1567.6  -0.082  0.93701   
## Week_13       -593.5     1796.2  -0.330  0.74957   
## Week_14        511.5     3748.5   0.136  0.89483   
## Week_15       3093.1     2687.9   1.151  0.28306   
## Week_16       1995.0     1163.6   1.715  0.12477   
## Week_17      -2041.0     1695.8  -1.204  0.26317   
## Week_18        845.6     1299.5   0.651  0.53344   
## Week_19       1770.3      813.4   2.176  0.06120 . 
## Week_20       1772.9     1813.0   0.978  0.35679   
## Week_21        948.4     1588.8   0.597  0.56708   
## Week_22       -988.1      998.6  -0.989  0.35141   
## Week_23      -3053.1     1549.2  -1.971  0.08425 . 
## Week_24       1165.0     1569.5   0.742  0.47915   
## Week_25      -1809.0     1952.4  -0.927  0.38128   
## Week_26       -495.9      538.9  -0.920  0.38437   
## Week_27       3532.2     1572.6   2.246  0.05490 . 
## Week_28        912.1     1560.7   0.584  0.57503   
## Week_29       2202.8     1353.7   1.627  0.14232   
## Week_30        447.8     1010.9   0.443  0.66954   
## Week_31      -4140.4     4445.2  -0.931  0.37888   
## Week_32        103.0     1317.1   0.078  0.93961   
## Week_33      -4513.0     2051.2  -2.200  0.05898 . 
## Week_34       4737.9     3538.5   1.339  0.21738   
## Week_35      -1711.8     2141.6  -0.799  0.44720   
## Week_36       4970.6     2338.9   2.125  0.06629 . 
## Week_37      -1614.6     3857.8  -0.419  0.68658   
## Week_38        178.6     2271.9   0.079  0.93928   
## Week_39      -1377.1     1908.0  -0.722  0.49099   
## Week_40      -2170.8     3086.7  -0.703  0.50183   
## Week_41        841.9     2852.7   0.295  0.77541   
## Week_42       4085.1     2375.3   1.720  0.12377   
## Week_43      -2221.6     2942.6  -0.755  0.47189   
## Week_44        150.5     2205.2   0.068  0.94728   
## Week_45      -3618.9     3516.8  -1.029  0.33357   
## Week_46       4366.6     3302.2   1.322  0.22261   
## Week_47      -1076.3     1494.1  -0.720  0.49184   
## Week_48       3038.5     1934.1   1.571  0.15482   
## Week_49        538.8     1854.6   0.291  0.77881   
## Week_50      -2114.1     1944.3  -1.087  0.30856   
## Week_51      -1203.2     1417.8  -0.849  0.42073   
## Week_52       1993.5     2465.0   0.809  0.44207   
## Week_53      -3387.0     1561.0  -2.170  0.06184 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13130 on 8 degrees of freedom
## Multiple R-squared:  0.9163, Adjusted R-squared:  0.3619 
## F-statistic: 1.653 on 53 and 8 DF,  p-value: 0.2305

## [1] "Results for crop: Raspberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7425.1 -1830.7   159.5  1640.6 10838.8 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 38575.24   13283.48   2.904   0.0198 *
## Week_1      -1403.29     950.18  -1.477   0.1780  
## Week_2        -75.63     656.59  -0.115   0.9111  
## Week_3      -1129.77     860.72  -1.313   0.2257  
## Week_4       2132.49    1501.89   1.420   0.1934  
## Week_5       -116.15     855.60  -0.136   0.8954  
## Week_6      -1094.49    1220.61  -0.897   0.3961  
## Week_7       2670.75    1591.40   1.678   0.1318  
## Week_8      -1066.28    1234.53  -0.864   0.4129  
## Week_9       1750.92    1142.32   1.533   0.1639  
## Week_10     -2646.91    1170.31  -2.262   0.0536 .
## Week_11      2260.02    1668.87   1.354   0.2127  
## Week_12     -2260.07    1068.21  -2.116   0.0673 .
## Week_13      1660.71    1223.96   1.357   0.2119  
## Week_14     -3561.38    2554.28  -1.394   0.2007  
## Week_15      2932.58    1831.57   1.601   0.1480  
## Week_16       -81.53     792.91  -0.103   0.9206  
## Week_17      1751.79    1155.56   1.516   0.1680  
## Week_18      -435.27     885.48  -0.492   0.6362  
## Week_19       358.92     554.26   0.648   0.5354  
## Week_20       957.48    1235.45   0.775   0.4606  
## Week_21       479.84    1082.64   0.443   0.6693  
## Week_22       191.51     680.45   0.281   0.7855  
## Week_23       270.30    1055.66   0.256   0.8044  
## Week_24       957.57    1069.49   0.895   0.3967  
## Week_25      -827.69    1330.44  -0.622   0.5512  
## Week_26        87.36     367.22   0.238   0.8179  
## Week_27     -1019.80    1071.64  -0.952   0.3692  
## Week_28      1946.71    1063.50   1.830   0.1046  
## Week_29      -162.84     922.43  -0.177   0.8643  
## Week_30       244.00     688.88   0.354   0.7323  
## Week_31     -5340.99    3029.03  -1.763   0.1159  
## Week_32     -1520.77     897.48  -1.694   0.1286  
## Week_33     -1147.44    1397.72  -0.821   0.4355  
## Week_34      3479.24    2411.22   1.443   0.1870  
## Week_35     -3175.18    1459.36  -2.176   0.0613 .
## Week_36      3175.92    1593.80   1.993   0.0814 .
## Week_37      2750.35    2628.79   1.046   0.3260  
## Week_38     -2153.98    1548.13  -1.391   0.2016  
## Week_39      -373.91    1300.15  -0.288   0.7810  
## Week_40      1410.62    2103.34   0.671   0.5213  
## Week_41     -1818.99    1943.88  -0.936   0.3768  
## Week_42     -1358.87    1618.59  -0.840   0.4255  
## Week_43       339.94    2005.14   0.170   0.8696  
## Week_44      2361.83    1502.64   1.572   0.1546  
## Week_45      -844.36    2396.41  -0.352   0.7337  
## Week_46      1400.81    2250.17   0.623   0.5509  
## Week_47      -434.00    1018.14  -0.426   0.6812  
## Week_48      -826.90    1317.96  -0.627   0.5479  
## Week_49       179.49    1263.78   0.142   0.8906  
## Week_50      -818.68    1324.90  -0.618   0.5538  
## Week_51       523.05     966.13   0.541   0.6030  
## Week_52     -1834.04    1679.71  -1.092   0.3067  
## Week_53      -330.80    1063.71  -0.311   0.7638  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8950 on 8 degrees of freedom
## Multiple R-squared:  0.885,  Adjusted R-squared:  0.1231 
## F-statistic: 1.162 on 53 and 8 DF,  p-value: 0.4452

## [1] "Results for crop: Strawberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9574.4 -2626.2  -828.6  2319.2 12062.2 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 96214.07   18287.79   5.261 0.000763 ***
## Week_1        864.57    1308.15   0.661 0.527240    
## Week_2       -647.67     903.94  -0.716 0.494067    
## Week_3       1316.81    1184.98   1.111 0.298740    
## Week_4       -216.05    2067.70  -0.104 0.919354    
## Week_5        -50.65    1177.93  -0.043 0.966754    
## Week_6      -2597.43    1680.45  -1.546 0.160764    
## Week_7       1128.01    2190.93   0.515 0.620575    
## Week_8       1744.02    1699.62   1.026 0.334855    
## Week_9      -1174.94    1572.67  -0.747 0.476387    
## Week_10        19.93    1611.20   0.012 0.990432    
## Week_11     -3106.88    2297.59  -1.352 0.213281    
## Week_12      -749.85    1470.64  -0.510 0.623899    
## Week_13       966.28    1685.06   0.573 0.582104    
## Week_14      1617.57    3516.56   0.460 0.657769    
## Week_15      2003.33    2521.58   0.794 0.449843    
## Week_16      1978.71    1091.62   1.813 0.107456    
## Week_17     -2610.72    1590.90  -1.641 0.139419    
## Week_18      1201.27    1219.06   0.985 0.353288    
## Week_19      2049.35     763.07   2.686 0.027682 *  
## Week_20      1696.67    1700.88   0.998 0.347722    
## Week_21      -414.95    1490.50  -0.278 0.787770    
## Week_22      -642.76     936.80  -0.686 0.512030    
## Week_23     -2279.89    1453.37  -1.569 0.155357    
## Week_24       964.27    1472.40   0.655 0.530909    
## Week_25     -2834.51    1831.66  -1.548 0.160329    
## Week_26      -253.21     505.56  -0.501 0.629964    
## Week_27      4813.40    1475.36   3.263 0.011484 *  
## Week_28       465.76    1464.15   0.318 0.758547    
## Week_29       947.86    1269.94   0.746 0.476794    
## Week_30       276.49     948.41   0.292 0.778061    
## Week_31      4293.72    4170.16   1.030 0.333305    
## Week_32       -20.41    1235.59  -0.017 0.987228    
## Week_33     -3697.88    1924.28  -1.922 0.090878 .  
## Week_34      7274.26    3319.60   2.191 0.059799 .  
## Week_35      -690.19    2009.15  -0.344 0.740057    
## Week_36      2965.91    2194.24   1.352 0.213451    
## Week_37     -5179.06    3619.13  -1.431 0.190304    
## Week_38       186.13    2131.36   0.087 0.932556    
## Week_39     -1639.98    1789.96  -0.916 0.386342    
## Week_40     -2550.36    2895.74  -0.881 0.404146    
## Week_41      2136.35    2676.20   0.798 0.447757    
## Week_42      5077.45    2228.36   2.279 0.052188 .  
## Week_43     -2317.80    2760.54  -0.840 0.425501    
## Week_44      -549.43    2068.73  -0.266 0.797272    
## Week_45     -7212.95    3299.22  -2.186 0.060271 .  
## Week_46      7210.22    3097.88   2.327 0.048352 *  
## Week_47       -70.61    1401.71  -0.050 0.961059    
## Week_48      3032.03    1814.48   1.671 0.133262    
## Week_49      1772.54    1739.88   1.019 0.338130    
## Week_50     -3077.15    1824.03  -1.687 0.130084    
## Week_51     -1938.64    1330.10  -1.458 0.183080    
## Week_52      2267.84    2312.51   0.981 0.355471    
## Week_53     -2077.35    1464.44  -1.419 0.193798    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12320 on 8 degrees of freedom
## Multiple R-squared:  0.9484, Adjusted R-squared:  0.6068 
## F-statistic: 2.776 on 53 and 8 DF,  p-value: 0.06325

Kelowna weekly

## [1] "NA value found at row 17 and column 47"
## [1] "NA value found at row 17 and column 48"
## [1] "NA value found at row 17 and column 49"
## [1] "NA value found at row 57 and column 55"
## [1] "There are 4  NA in the matrix X in Kelowna station"
## [1] "Results for crop: Apples"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -30154.8  -6135.8    712.3   7224.2  26419.6 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 254454.9    70907.1   3.589  0.01152 * 
## Week_1        9439.3     3995.8   2.362  0.05611 . 
## Week_2       -3840.2     2552.3  -1.505  0.18313   
## Week_3       -4066.3     4223.3  -0.963  0.37281   
## Week_4        6566.7     3577.6   1.836  0.11610   
## Week_5       -6977.7     3332.6  -2.094  0.08117 . 
## Week_6        2346.9     2280.3   1.029  0.34306   
## Week_7       -4405.8     2991.0  -1.473  0.19118   
## Week_8        6088.8     3069.5   1.984  0.09455 . 
## Week_9       -2005.2     4852.3  -0.413  0.69379   
## Week_10       -197.9     3949.4  -0.050  0.96166   
## Week_11      -5271.4     3296.5  -1.599  0.16092   
## Week_12       -314.6     3867.2  -0.081  0.93780   
## Week_13       2065.0     5988.0   0.345  0.74197   
## Week_14       6474.1     5175.1   1.251  0.25749   
## Week_15      15634.6     4786.3   3.267  0.01711 * 
## Week_16      -1074.1     4244.7  -0.253  0.80867   
## Week_17     -24290.2     4655.2  -5.218  0.00198 **
## Week_18       2128.1     3328.9   0.639  0.54626   
## Week_19       5076.9     3173.8   1.600  0.16080   
## Week_20       1830.8     2936.3   0.624  0.55590   
## Week_21       5182.3     3423.1   1.514  0.18082   
## Week_22     -12541.4     3919.7  -3.200  0.01861 * 
## Week_23      -1653.5     3275.6  -0.505  0.63171   
## Week_24       9563.5     3834.2   2.494  0.04689 * 
## Week_25      -8258.8     3408.0  -2.423  0.05162 . 
## Week_26       2224.8     1214.6   1.832  0.11670   
## Week_27       1278.9     5758.2   0.222  0.83160   
## Week_28       5099.3     6685.0   0.763  0.47449   
## Week_29       -308.1     8341.8  -0.037  0.97174   
## Week_30      -1188.9     6547.5  -0.182  0.86189   
## Week_31       1457.7     5650.9   0.258  0.80506   
## Week_32      -2317.8     9721.5  -0.238  0.81949   
## Week_33      17332.7     7236.2   2.395  0.05364 . 
## Week_34       6067.5    11218.0   0.541  0.60807   
## Week_35       7128.2     6922.9   1.030  0.34288   
## Week_36     -34922.4     6136.6  -5.691  0.00127 **
## Week_37       9970.6     5411.6   1.842  0.11499   
## Week_38       1583.9     5484.0   0.289  0.78244   
## Week_39       1229.5     5659.8   0.217  0.83523   
## Week_40       3828.9     6468.2   0.592  0.57549   
## Week_41     -13650.5     6388.0  -2.137  0.07648 . 
## Week_42       3479.1     7900.1   0.440  0.67508   
## Week_43       5254.8     8613.5   0.610  0.56419   
## Week_44       2164.5     3729.6   0.580  0.58279   
## Week_45       2399.5     6028.6   0.398  0.70439   
## Week_46      -4232.3     4628.8  -0.914  0.39580   
## Week_47       5739.4     4220.4   1.360  0.22273   
## Week_48       2652.9     3446.3   0.770  0.47065   
## Week_49      -5711.5     3232.3  -1.767  0.12765   
## Week_50       2010.6     5630.5   0.357  0.73325   
## Week_51       4146.0     3824.5   1.084  0.31996   
## Week_52       1642.3     3186.3   0.515  0.62468   
## Week_53      -1057.9     2569.2  -0.412  0.69481   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35570 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.9606, Adjusted R-squared:  0.6121 
## F-statistic: 2.757 on 53 and 6 DF,  p-value: 0.1008

## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -4248  -1103     18   1152   3842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 33590.31   10323.46   3.254  0.01738 * 
## Week_1       1054.09     581.76   1.812  0.11996   
## Week_2       -664.55     371.59  -1.788  0.12393   
## Week_3       -277.34     614.87  -0.451  0.66779   
## Week_4        586.74     520.87   1.126  0.30300   
## Week_5       -715.63     485.20  -1.475  0.19068   
## Week_6        369.49     331.98   1.113  0.30831   
## Week_7       -702.58     435.47  -1.613  0.15779   
## Week_8        913.15     446.89   2.043  0.08704 . 
## Week_9       -553.82     706.46  -0.784  0.46289   
## Week_10       198.69     575.00   0.346  0.74148   
## Week_11      -675.12     479.94  -1.407  0.20915   
## Week_12       594.93     563.03   1.057  0.33134   
## Week_13      -550.34     871.79  -0.631  0.55114   
## Week_14      1322.64     753.44   1.755  0.12971   
## Week_15      1404.07     696.85   2.015  0.09054 . 
## Week_16       -50.45     617.99  -0.082  0.93759   
## Week_17     -2469.89     677.76  -3.644  0.01078 * 
## Week_18       138.19     484.65   0.285  0.78513   
## Week_19       588.05     462.08   1.273  0.25024   
## Week_20       143.15     427.50   0.335  0.74913   
## Week_21       553.64     498.37   1.111  0.30914   
## Week_22     -1078.15     570.67  -1.889  0.10776   
## Week_23        94.57     476.90   0.198  0.84936   
## Week_24       422.13     558.22   0.756  0.47816   
## Week_25      -389.16     496.17  -0.784  0.46268   
## Week_26       160.19     176.83   0.906  0.39991   
## Week_27       140.03     838.34   0.167  0.87283   
## Week_28      -198.91     973.28  -0.204  0.84482   
## Week_29       408.55    1214.49   0.336  0.74802   
## Week_30      -318.03     953.26  -0.334  0.75001   
## Week_31      -392.14     822.73  -0.477  0.65049   
## Week_32       724.27    1415.37   0.512  0.62713   
## Week_33      1425.23    1053.53   1.353  0.22487   
## Week_34       768.77    1633.24   0.471  0.65448   
## Week_35       474.34    1007.91   0.471  0.65453   
## Week_36     -3318.04     893.44  -3.714  0.00992 **
## Week_37       940.77     787.88   1.194  0.27752   
## Week_38       593.36     798.42   0.743  0.48546   
## Week_39       798.80     824.02   0.969  0.36979   
## Week_40       762.89     941.72   0.810  0.44881   
## Week_41     -1889.89     930.04  -2.032  0.08841 . 
## Week_42       -65.53    1150.18  -0.057  0.95641   
## Week_43        47.78    1254.05   0.038  0.97084   
## Week_44       461.84     543.00   0.851  0.42766   
## Week_45      -261.94     877.71  -0.298  0.77543   
## Week_46      -375.77     673.92  -0.558  0.59730   
## Week_47       219.60     614.45   0.357  0.73304   
## Week_48       -28.28     501.76  -0.056  0.95689   
## Week_49      -530.60     470.60  -1.128  0.30259   
## Week_50       464.74     819.75   0.567  0.59133   
## Week_51       348.08     556.81   0.625  0.55490   
## Week_52       324.56     463.90   0.700  0.51036   
## Week_53      -280.66     374.06  -0.750  0.48145   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5178 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.9289, Adjusted R-squared:  0.3007 
## F-statistic: 1.479 on 53 and 6 DF,  p-value: 0.3307

## [1] "Results for crop: Maize (corn)"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14723.5  -3382.8    229.2   4196.2  13326.0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 105692.57   31850.27   3.318   0.0160 *
## Week_1        2125.89    1794.85   1.184   0.2810  
## Week_2       -1713.57    1146.45  -1.495   0.1856  
## Week_3         527.42    1897.01   0.278   0.7903  
## Week_4         885.51    1607.00   0.551   0.6015  
## Week_5       -1191.23    1496.94  -0.796   0.4565  
## Week_6         388.69    1024.25   0.379   0.7174  
## Week_7        -858.96    1343.53  -0.639   0.5462  
## Week_8        1620.62    1378.77   1.175   0.2844  
## Week_9       -1896.36    2179.59  -0.870   0.4177  
## Week_10       1186.41    1774.00   0.669   0.5285  
## Week_11      -1862.62    1480.74  -1.258   0.2552  
## Week_12       1480.00    1737.07   0.852   0.4269  
## Week_13      -2149.96    2689.69  -0.799   0.4546  
## Week_14       3958.42    2324.55   1.703   0.1395  
## Week_15       1740.71    2149.94   0.810   0.4490  
## Week_16       1125.31    1906.64   0.590   0.5766  
## Week_17      -4571.97    2091.04  -2.186   0.0714 .
## Week_18       -526.76    1495.27  -0.352   0.7367  
## Week_19       1760.62    1425.62   1.235   0.2630  
## Week_20        814.17    1318.93   0.617   0.5597  
## Week_21       1208.20    1537.60   0.786   0.4619  
## Week_22      -3277.87    1760.65  -1.862   0.1120  
## Week_23        387.68    1471.34   0.263   0.8010  
## Week_24       1790.94    1722.25   1.040   0.3385  
## Week_25      -2411.54    1530.79  -1.575   0.1662  
## Week_26       1273.79     545.56   2.335   0.0583 .
## Week_27       -817.11    2586.47  -0.316   0.7628  
## Week_28         58.59    3002.79   0.020   0.9851  
## Week_29       2220.45    3746.98   0.593   0.5751  
## Week_30      -1671.65    2941.03  -0.568   0.5904  
## Week_31        658.52    2538.31   0.259   0.8040  
## Week_32       1744.70    4366.73   0.400   0.7033  
## Week_33       2876.75    3250.40   0.885   0.4102  
## Week_34       4771.24    5038.91   0.947   0.3803  
## Week_35       1581.16    3109.63   0.508   0.6293  
## Week_36      -7086.68    2756.46  -2.571   0.0423 *
## Week_37       1988.60    2430.80   0.818   0.4446  
## Week_38       -107.79    2463.32  -0.044   0.9665  
## Week_39        981.25    2542.31   0.386   0.7128  
## Week_40        853.83    2905.41   0.294   0.7788  
## Week_41      -3789.38    2869.38  -1.321   0.2348  
## Week_42        331.13    3548.57   0.093   0.9287  
## Week_43       -598.94    3869.04  -0.155   0.8821  
## Week_44       1464.76    1675.28   0.874   0.4156  
## Week_45      -1264.21    2707.93  -0.467   0.6571  
## Week_46       -960.49    2079.20  -0.462   0.6604  
## Week_47       -120.36    1895.73  -0.063   0.9514  
## Week_48         82.81    1548.04   0.053   0.9591  
## Week_49       -633.68    1451.90  -0.436   0.6778  
## Week_50       2907.58    2529.13   1.150   0.2940  
## Week_51       -510.12    1717.89  -0.297   0.7765  
## Week_52       1543.22    1431.23   1.078   0.3224  
## Week_53       -770.75    1154.06  -0.668   0.5291  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15980 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.9185, Adjusted R-squared:  0.1982 
## F-statistic: 1.275 on 53 and 6 DF,  p-value: 0.4139

## [1] "Results for crop: Peaches and nectarines"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -6787  -2096    146   1982  12627 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 120001.02   20945.20   5.729  0.00123 **
## Week_1        1001.52    1180.32   0.849  0.42870   
## Week_2          85.80     753.92   0.114  0.91310   
## Week_3       -1457.72    1247.50  -1.169  0.28692   
## Week_4        1821.07    1056.79   1.723  0.13562   
## Week_5       -1664.96     984.41  -1.691  0.14173   
## Week_6          84.04     673.56   0.125  0.90479   
## Week_7        -421.76     883.52  -0.477  0.65000   
## Week_8         973.76     906.70   1.074  0.32412   
## Week_9        -161.96    1433.33  -0.113  0.91372   
## Week_10       -288.02    1166.61  -0.247  0.81323   
## Week_11       -638.92     973.76  -0.656  0.53607   
## Week_12          8.31    1142.32   0.007  0.99443   
## Week_13        527.75    1768.78   0.298  0.77548   
## Week_14        530.75    1528.66   0.347  0.74029   
## Week_15       2076.98    1413.83   1.469  0.19221   
## Week_16       -548.24    1253.83  -0.437  0.67723   
## Week_17      -3229.28    1375.10  -2.348  0.05718 . 
## Week_18        441.39     983.31   0.449  0.66928   
## Week_19       -197.83     937.51  -0.211  0.83986   
## Week_20        307.69     867.35   0.355  0.73491   
## Week_21        888.25    1011.15   0.878  0.41348   
## Week_22      -1571.37    1157.83  -1.357  0.22356   
## Week_23       -713.96     967.58  -0.738  0.48843   
## Week_24        466.58    1132.57   0.412  0.69468   
## Week_25        494.08    1006.67   0.491  0.64099   
## Week_26       -399.71     358.77  -1.114  0.30786   
## Week_27       1399.70    1700.90   0.823  0.44202   
## Week_28        536.30    1974.68   0.272  0.79504   
## Week_29        717.21    2464.07   0.291  0.78080   
## Week_30        448.10    1934.06   0.232  0.82448   
## Week_31      -1275.62    1669.23  -0.764  0.47372   
## Week_32       2028.87    2871.63   0.707  0.50636   
## Week_33       1298.57    2137.51   0.608  0.56578   
## Week_34      -2967.45    3313.66  -0.896  0.40500   
## Week_35       2001.87    2044.94   0.979  0.36542   
## Week_36      -5875.36    1812.69  -3.241  0.01766 * 
## Week_37       2532.70    1598.53   1.584  0.16420   
## Week_38       -139.24    1619.92  -0.086  0.93430   
## Week_39        903.11    1671.86   0.540  0.60852   
## Week_40        822.39    1910.64   0.430  0.68192   
## Week_41       -892.41    1886.95  -0.473  0.65297   
## Week_42        675.40    2333.59   0.289  0.78200   
## Week_43       2540.30    2544.34   0.998  0.35663   
## Week_44       -522.64    1101.69  -0.474  0.65198   
## Week_45        366.61    1780.77   0.206  0.84370   
## Week_46        681.99    1367.31   0.499  0.63569   
## Week_47        527.08    1246.66   0.423  0.68718   
## Week_48       1045.80    1018.01   1.027  0.34390   
## Week_49      -1266.77     954.79  -1.327  0.23285   
## Week_50        241.37    1663.19   0.145  0.88937   
## Week_51        830.84    1129.71   0.735  0.48981   
## Week_52       -582.00     941.20  -0.618  0.55906   
## Week_53        473.67     758.93   0.624  0.55551   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10510 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.8785, Adjusted R-squared:  -0.1946 
## F-statistic: 0.8187 on 53 and 6 DF,  p-value: 0.6899

## [1] "Results for crop: Wheat"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4257.5  -909.1    62.1  1143.0  4236.3 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 29967.882  10530.540   2.846   0.0293 *
## Week_1        810.150    593.426   1.365   0.2212  
## Week_2       -652.709    379.046  -1.722   0.1359  
## Week_3        131.664    627.203   0.210   0.8407  
## Week_4        193.406    531.316   0.364   0.7283  
## Week_5       -450.415    494.929  -0.910   0.3979  
## Week_6        256.779    338.644   0.758   0.4770  
## Week_7       -664.789    444.206  -1.497   0.1851  
## Week_8        830.048    455.858   1.821   0.1185  
## Week_9       -403.779    720.629  -0.560   0.5956  
## Week_10       159.243    586.531   0.271   0.7951  
## Week_11      -714.886    489.572  -1.460   0.1945  
## Week_12       505.785    574.321   0.881   0.4124  
## Week_13      -373.711    889.282  -0.420   0.6889  
## Week_14      1195.488    768.557   1.555   0.1708  
## Week_15       390.651    710.826   0.550   0.6025  
## Week_16       696.135    630.385   1.104   0.3118  
## Week_17     -2186.647    691.354  -3.163   0.0195 *
## Week_18       138.100    494.376   0.279   0.7894  
## Week_19       704.842    471.349   1.495   0.1854  
## Week_20       169.868    436.073   0.390   0.7103  
## Week_21       525.853    508.371   1.034   0.3408  
## Week_22      -838.146    582.118  -1.440   0.2000  
## Week_23      -210.753    486.465  -0.433   0.6800  
## Week_24       533.804    569.420   0.937   0.3847  
## Week_25      -462.799    506.121  -0.914   0.3958  
## Week_26       288.195    180.377   1.598   0.1612  
## Week_27      -413.028    855.157  -0.483   0.6462  
## Week_28       129.790    992.801   0.131   0.9003  
## Week_29        55.489   1238.850   0.045   0.9657  
## Week_30      -425.918    972.382  -0.438   0.6767  
## Week_31        86.052    839.231   0.103   0.9217  
## Week_32      1269.551   1443.757   0.879   0.4130  
## Week_33       773.410   1074.667   0.720   0.4988  
## Week_34      1592.584   1665.997   0.956   0.3760  
## Week_35        -2.384   1028.127  -0.002   0.9982  
## Week_36     -2857.637    911.358  -3.136   0.0202 *
## Week_37       684.519    803.686   0.852   0.4270  
## Week_38       563.159    814.440   0.691   0.5151  
## Week_39       480.921    840.554   0.572   0.5880  
## Week_40       469.918    960.605   0.489   0.6421  
## Week_41     -1648.595    948.694  -1.738   0.1329  
## Week_42      1095.947   1173.252   0.934   0.3863  
## Week_43      -744.176   1279.207  -0.582   0.5819  
## Week_44       450.345    553.891   0.813   0.4472  
## Week_45      -261.521    895.313  -0.292   0.7800  
## Week_46        30.213    687.438   0.044   0.9664  
## Week_47       164.912    626.777   0.263   0.8013  
## Week_48      -232.763    511.822  -0.455   0.6653  
## Week_49      -519.140    480.037  -1.081   0.3210  
## Week_50       525.198    836.196   0.628   0.5531  
## Week_51       333.531    567.981   0.587   0.5785  
## Week_52       197.264    473.202   0.417   0.6913  
## Week_53      -308.792    381.563  -0.809   0.4492  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5282 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.9297, Adjusted R-squared:  0.3085 
## F-statistic: 1.497 on 53 and 6 DF,  p-value: 0.3244

## [1] "Results for crop: Wine"
## [1] "No data available for this crop."
## [1] "Results for crop: Blueberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3528.5  -943.8   -85.9  1063.8  3455.6 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 60077.04    9333.07   6.437 0.000665 ***
## Week_1         15.02     525.94   0.029 0.978149    
## Week_2       -354.00     335.94  -1.054 0.332572    
## Week_3        717.52     555.88   1.291 0.244281    
## Week_4       -340.72     470.90  -0.724 0.496568    
## Week_5        217.01     438.65   0.495 0.638393    
## Week_6       -365.02     300.14  -1.216 0.269581    
## Week_7        165.25     393.69   0.420 0.689288    
## Week_8         29.36     404.02   0.073 0.944433    
## Week_9       -166.57     638.68  -0.261 0.802968    
## Week_10      -234.08     519.83  -0.450 0.668306    
## Week_11       169.53     433.90   0.391 0.709506    
## Week_12       262.84     509.01   0.516 0.624073    
## Week_13     -1076.35     788.16  -1.366 0.221027    
## Week_14      1078.02     681.16   1.583 0.164596    
## Week_15     -1559.96     630.00  -2.476 0.048056 *  
## Week_16      1827.43     558.70   3.271 0.017016 *  
## Week_17       386.71     612.74   0.631 0.551235    
## Week_18      -492.61     438.16  -1.124 0.303853    
## Week_19        11.51     417.75   0.028 0.978914    
## Week_20       989.29     386.49   2.560 0.042925 *  
## Week_21       -57.21     450.56  -0.127 0.903112    
## Week_22       436.35     515.92   0.846 0.430113    
## Week_23       317.56     431.15   0.737 0.489182    
## Week_24      -733.99     504.67  -1.454 0.196068    
## Week_25      -240.76     448.57  -0.537 0.610760    
## Week_26       209.51     159.87   1.311 0.237947    
## Week_27       -14.21     757.91  -0.019 0.985648    
## Week_28      -131.89     879.91  -0.150 0.885762    
## Week_29       206.71    1097.98   0.188 0.856876    
## Week_30      -974.75     861.81  -1.131 0.301207    
## Week_31      1077.29     743.80   1.448 0.197681    
## Week_32       947.29    1279.58   0.740 0.487056    
## Week_33     -1773.74     952.46  -1.862 0.111871    
## Week_34      3870.94    1476.55   2.622 0.039501 *  
## Week_35       675.84     911.21   0.742 0.486281    
## Week_36     -1057.40     807.72  -1.309 0.238394    
## Week_37       317.53     712.30   0.446 0.671388    
## Week_38       841.74     721.83   1.166 0.287815    
## Week_39     -1685.51     744.97  -2.263 0.064326 .  
## Week_40       103.02     851.37   0.121 0.907638    
## Week_41      1151.08     840.81   1.369 0.220034    
## Week_42      1809.87    1039.84   1.741 0.132413    
## Week_43      -637.56    1133.74  -0.562 0.594247    
## Week_44       724.07     490.91   1.475 0.190668    
## Week_45     -1348.15     793.50  -1.699 0.140237    
## Week_46       794.57     609.27   1.304 0.239978    
## Week_47      -391.25     555.50  -0.704 0.507634    
## Week_48      -284.07     453.62  -0.626 0.554229    
## Week_49       380.32     425.45   0.894 0.405789    
## Week_50       385.14     741.11   0.520 0.621892    
## Week_51       -95.59     503.39  -0.190 0.855654    
## Week_52        44.52     419.39   0.106 0.918929    
## Week_53      -356.12     338.17  -1.053 0.332864    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4681 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.9412, Adjusted R-squared:  0.4215 
## F-statistic: 1.811 on 53 and 6 DF,  p-value: 0.2339

## [1] "Results for crop: Grapes"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9439.1 -2055.9  -257.6  2199.7  7010.1 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 68156.459  21733.187   3.136  0.02017 * 
## Week_1       2188.683   1224.726   1.787  0.12415   
## Week_2      -1597.258    782.284  -2.042  0.08723 . 
## Week_3       -942.923   1294.437  -0.728  0.49378   
## Week_4       1141.234   1096.544   1.041  0.33810   
## Week_5       -377.968   1021.447  -0.370  0.72407   
## Week_6        848.260    698.902   1.214  0.27046   
## Week_7      -2028.193    916.763  -2.212  0.06892 . 
## Week_8       2191.678    940.812   2.330  0.05868 . 
## Week_9      -1646.316   1487.251  -1.107  0.31071   
## Week_10      -883.528   1210.498  -0.730  0.49296   
## Week_11       227.101   1010.390   0.225  0.82962   
## Week_12      1723.757   1185.298   1.454  0.19610   
## Week_13     -2866.898   1835.322  -1.562  0.16930   
## Week_14      2192.277   1586.166   1.382  0.21619   
## Week_15      2605.230   1467.021   1.776  0.12610   
## Week_16       236.891   1301.004   0.182  0.86151   
## Week_17     -5458.131   1426.834  -3.825  0.00871 **
## Week_18      2930.105   1020.306   2.872  0.02836 * 
## Week_19       379.006    972.781   0.390  0.71028   
## Week_20       667.900    899.978   0.742  0.48603   
## Week_21       319.439   1049.189   0.304  0.77106   
## Week_22      -706.750   1201.390  -0.588  0.57780   
## Week_23       576.682   1003.979   0.574  0.58657   
## Week_24      -234.974   1175.183  -0.200  0.84813   
## Week_25     -1749.385   1044.545  -1.675  0.14500   
## Week_26       691.304    372.267   1.857  0.11269   
## Week_27      -572.160   1764.893  -0.324  0.75680   
## Week_28       -84.217   2048.967  -0.041  0.96855   
## Week_29      1838.951   2556.769   0.719  0.49903   
## Week_30     -1449.662   2006.826  -0.722  0.49725   
## Week_31     -1477.208   1732.026  -0.853  0.42646   
## Week_32     -1827.702   2979.662  -0.613  0.56213   
## Week_33      3005.513   2217.924   1.355  0.22418   
## Week_34      5139.220   3438.326   1.495  0.18562   
## Week_35      1735.325   2121.874   0.818  0.44471   
## Week_36     -4388.823   1880.883  -2.333  0.05837 . 
## Week_37      2658.598   1658.667   1.603  0.16009   
## Week_38       311.665   1680.862   0.185  0.85901   
## Week_39      2190.043   1734.756   1.262  0.25363   
## Week_40     -1424.282   1982.521  -0.718  0.49951   
## Week_41     -3150.295   1957.938  -1.609  0.15874   
## Week_42      -997.026   2421.387  -0.412  0.69482   
## Week_43      -503.172   2640.060  -0.191  0.85513   
## Week_44       810.417   1143.133   0.709  0.50496   
## Week_45      -539.623   1847.769  -0.292  0.78009   
## Week_46     -1509.091   1418.752  -1.064  0.32840   
## Week_47      -667.043   1293.557  -0.516  0.62453   
## Week_48       913.235   1056.311   0.865  0.42050   
## Week_49         7.926    990.713   0.008  0.99388   
## Week_50       979.602   1725.762   0.568  0.59087   
## Week_51       -57.064   1172.212  -0.049  0.96275   
## Week_52      2582.939    976.605   2.645  0.03829 * 
## Week_53     -2250.581    787.479  -2.858  0.02888 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10900 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.9517, Adjusted R-squared:  0.5249 
## F-statistic:  2.23 on 53 and 6 DF,  p-value: 0.1572

## [1] "Results for crop: Raspberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5109.1 -1735.2   102.4  1843.0  6184.9 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 46765.75   15381.26   3.040  0.02279 * 
## Week_1        934.18     866.78   1.078  0.32255   
## Week_2       -124.30     553.65  -0.225  0.82981   
## Week_3       -828.83     916.11  -0.905  0.40048   
## Week_4        542.37     776.06   0.699  0.51079   
## Week_5       -959.23     722.91  -1.327  0.23280   
## Week_6       1208.17     494.63   2.443  0.05030 . 
## Week_7       -566.20     648.82  -0.873  0.41640   
## Week_8       -356.02     665.84  -0.535  0.61208   
## Week_9        746.36    1052.57   0.709  0.50488   
## Week_10      -184.80     856.71  -0.216  0.83636   
## Week_11      -186.98     715.08  -0.261  0.80247   
## Week_12       304.49     838.87   0.363  0.72907   
## Week_13     -1704.26    1298.91  -1.312  0.23746   
## Week_14       615.22    1122.58   0.548  0.60344   
## Week_15      3881.69    1038.26   3.739  0.00964 **
## Week_16     -2124.09     920.76  -2.307  0.06053 . 
## Week_17     -2163.89    1009.82  -2.143  0.07585 . 
## Week_18       644.20     722.10   0.892  0.40668   
## Week_19       572.37     688.47   0.831  0.43759   
## Week_20     -1126.34     636.94  -1.768  0.12741   
## Week_21       184.23     742.54   0.248  0.81233   
## Week_22      -301.90     850.26  -0.355  0.73469   
## Week_23      2346.29     710.55   3.302  0.01637 * 
## Week_24       296.40     831.71   0.356  0.73376   
## Week_25     -1459.61     739.26  -1.974  0.09576 . 
## Week_26       130.54     263.46   0.495  0.63790   
## Week_27       178.04    1249.07   0.143  0.89132   
## Week_28      1385.44    1450.12   0.955  0.37627   
## Week_29     -1226.06    1809.51  -0.678  0.52330   
## Week_30      1847.09    1420.29   1.301  0.24114   
## Week_31      -855.39    1225.81  -0.698  0.51141   
## Week_32     -4285.81    2108.80  -2.032  0.08838 . 
## Week_33      3378.40    1569.69   2.152  0.07487 . 
## Week_34      1709.14    2433.41   0.702  0.50877   
## Week_35     -2221.66    1501.72  -1.479  0.18952   
## Week_36     -1743.00    1331.16  -1.309  0.23831   
## Week_37       934.09    1173.89   0.796  0.45651   
## Week_38      1060.48    1189.60   0.891  0.40701   
## Week_39      1056.07    1227.74   0.860  0.42273   
## Week_40      -238.15    1403.09  -0.170  0.87080   
## Week_41       198.43    1385.69   0.143  0.89082   
## Week_42     -2870.61    1713.69  -1.675  0.14493   
## Week_43      4173.14    1868.45   2.233  0.06695 . 
## Week_44      -820.15     809.03  -1.014  0.34983   
## Week_45      -446.36    1307.72  -0.341  0.74449   
## Week_46      -848.81    1004.10  -0.845  0.43033   
## Week_47      1047.42     915.49   1.144  0.29616   
## Week_48      -243.87     747.58  -0.326  0.75534   
## Week_49        69.04     701.16   0.098  0.92477   
## Week_50     -1356.34    1221.38  -1.111  0.30929   
## Week_51       449.93     829.61   0.542  0.60712   
## Week_52      1045.16     691.17   1.512  0.18125   
## Week_53     -1220.27     557.32  -2.190  0.07112 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7715 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.9336, Adjusted R-squared:  0.3473 
## F-statistic: 1.592 on 53 and 6 DF,  p-value: 0.2929

## [1] "Results for crop: Strawberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10104.8  -2583.3   -389.8   3367.5   7371.2 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  96670.63   25554.74   3.783  0.00915 **
## Week_1        3451.33    1440.08   2.397  0.05354 . 
## Week_2       -1520.17     919.84  -1.653  0.14949   
## Week_3       -1670.56    1522.05  -1.098  0.31447   
## Week_4        2831.99    1289.36   2.196  0.07045 . 
## Week_5       -2441.82    1201.06  -2.033  0.08829 . 
## Week_6          70.14     821.80   0.085  0.93476   
## Week_7        -398.52    1077.97  -0.370  0.72430   
## Week_8        1010.82    1106.24   0.914  0.39609   
## Week_9        -764.43    1748.77  -0.437  0.67731   
## Week_10       -188.55    1423.35  -0.132  0.89894   
## Week_11      -1134.97    1188.06  -0.955  0.37631   
## Week_12       1074.31    1393.72   0.771  0.47007   
## Week_13      -1405.91    2158.04  -0.651  0.53888   
## Week_14       2414.63    1865.08   1.295  0.24303   
## Week_15       3325.83    1724.98   1.928  0.10212   
## Week_16       1678.40    1529.77   1.097  0.31464   
## Week_17      -7583.73    1677.73  -4.520  0.00402 **
## Week_18       1082.16    1199.72   0.902  0.40181   
## Week_19       1245.47    1143.83   1.089  0.31801   
## Week_20       1453.33    1058.23   1.373  0.21875   
## Week_21        753.84    1233.68   0.611  0.56358   
## Week_22      -2476.33    1412.64  -1.753  0.13015   
## Week_23        189.76    1180.52   0.161  0.87757   
## Week_24       1236.00    1381.83   0.894  0.40552   
## Week_25      -2461.33    1228.22  -2.004  0.09192 . 
## Week_26        919.70     437.73   2.101  0.08036 . 
## Week_27       1493.06    2075.23   0.719  0.49890   
## Week_28        533.91    2409.26   0.222  0.83197   
## Week_29       2307.32    3006.35   0.767  0.47191   
## Week_30      -2755.85    2359.70  -1.168  0.28716   
## Week_31        149.63    2036.58   0.073  0.94382   
## Week_32        439.09    3503.60   0.125  0.90436   
## Week_33       3293.80    2607.92   1.263  0.25345   
## Week_34       4976.79    4042.92   1.231  0.26439   
## Week_35       4389.25    2494.98   1.759  0.12903   
## Week_36     -10722.99    2211.62  -4.848  0.00286 **
## Week_37       3373.03    1950.33   1.729  0.13445   
## Week_38       1223.21    1976.42   0.619  0.55873   
## Week_39       1973.64    2039.79   0.968  0.37063   
## Week_40       -761.51    2331.13  -0.327  0.75501   
## Week_41      -2030.78    2302.22  -0.882  0.41167   
## Week_42      -1113.27    2847.16  -0.391  0.70930   
## Week_43       1810.92    3104.29   0.583  0.58090   
## Week_44       1513.11    1344.14   1.126  0.30329   
## Week_45      -3075.01    2172.68  -1.415  0.20673   
## Week_46       -537.24    1668.22  -0.322  0.75835   
## Week_47       1197.76    1521.02   0.787  0.46097   
## Week_48        320.67    1242.05   0.258  0.80490   
## Week_49      -1484.16    1164.92  -1.274  0.24977   
## Week_50       2038.08    2029.22   1.004  0.35397   
## Week_51        951.15    1378.33   0.690  0.51594   
## Week_52       1096.07    1148.33   0.954  0.37669   
## Week_53      -1152.40     925.95  -1.245  0.25970   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12820 on 6 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.953,  Adjusted R-squared:  0.5378 
## F-statistic: 2.295 on 53 and 6 DF,  p-value: 0.1483

Abbotsford monthly

## [1] "There are 6  NA in the matrix X in Abbotsford station"
## [1] "Results for crop: Apples"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -83128 -40055  -1379  28889 110283 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 139811.0     7999.8  17.477   <2e-16 ***
## Month_1        521.0      884.1   0.589   0.5583    
## Month_2       -913.1     1016.1  -0.899   0.3732    
## Month_3        997.3      785.2   1.270   0.2101    
## Month_4       1051.4      607.8   1.730   0.0900 .  
## Month_5        936.8      511.1   1.833   0.0729 .  
## Month_6        621.2      320.9   1.936   0.0587 .  
## Month_7       1063.9      478.5   2.223   0.0308 *  
## Month_8       1917.5     1178.1   1.628   0.1100    
## Month_9       4248.3     1656.4   2.565   0.0134 *  
## Month_10     -2634.4     2914.7  -0.904   0.3705    
## Month_11     -1132.8     2020.3  -0.561   0.5776    
## Month_12       415.0     1048.2   0.396   0.6939    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 47600 on 49 degrees of freedom
## Multiple R-squared:  0.4567, Adjusted R-squared:  0.3237 
## F-statistic: 3.433 on 12 and 49 DF,  p-value: 0.001068

## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10085.2  -3371.7    504.3   3570.7  11400.2 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 24425.49     919.85  26.554   <2e-16 ***
## Month_1       -49.89     101.66  -0.491   0.6258    
## Month_2       -29.44     116.83  -0.252   0.8021    
## Month_3       199.03      90.29   2.204   0.0322 *  
## Month_4       120.83      69.89   1.729   0.0901 .  
## Month_5        97.11      58.77   1.652   0.1048    
## Month_6        18.59      36.90   0.504   0.6167    
## Month_7        63.99      55.02   1.163   0.2505    
## Month_8       311.02     135.46   2.296   0.0260 *  
## Month_9       339.10     190.46   1.780   0.0812 .  
## Month_10     -445.52     335.15  -1.329   0.1899    
## Month_11     -236.64     232.31  -1.019   0.3134    
## Month_12       54.12     120.53   0.449   0.6554    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5474 on 49 degrees of freedom
## Multiple R-squared:  0.3811, Adjusted R-squared:  0.2296 
## F-statistic: 2.515 on 12 and 49 DF,  p-value: 0.01158

## [1] "Results for crop: Maize (corn)"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -31122  -8239   -518   7236  32357 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 60095.97    2409.55  24.941   <2e-16 ***
## Month_1       191.94     266.29   0.721   0.4745    
## Month_2      -270.34     306.04  -0.883   0.3814    
## Month_3       383.27     236.50   1.621   0.1115    
## Month_4       244.95     183.08   1.338   0.1871    
## Month_5       179.89     153.95   1.169   0.2483    
## Month_6       242.49      96.66   2.509   0.0155 *  
## Month_7       369.64     144.12   2.565   0.0134 *  
## Month_8       912.49     354.85   2.572   0.0132 *  
## Month_9       934.74     498.91   1.874   0.0670 .  
## Month_10    -1069.26     877.91  -1.218   0.2291    
## Month_11     -405.27     608.52  -0.666   0.5085    
## Month_12      359.04     315.73   1.137   0.2610    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14340 on 49 degrees of freedom
## Multiple R-squared:  0.493,  Adjusted R-squared:  0.3689 
## F-statistic: 3.971 on 12 and 49 DF,  p-value: 0.0002775

## [1] "Results for crop: Peaches and nectarines"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16719.9  -4951.4    841.6   4278.2  14175.1 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 92702.49    1276.14  72.643  < 2e-16 ***
## Month_1       125.12     141.03   0.887 0.379328    
## Month_2       -19.87     162.09  -0.123 0.902953    
## Month_3       330.37     125.26   2.638 0.011158 *  
## Month_4       115.13      96.96   1.187 0.240819    
## Month_5       -77.89      81.53  -0.955 0.344081    
## Month_6        14.04      51.19   0.274 0.785021    
## Month_7       301.68      76.33   3.953 0.000248 ***
## Month_8      -233.37     187.93  -1.242 0.220240    
## Month_9       329.03     264.23   1.245 0.218975    
## Month_10     -398.92     464.96  -0.858 0.395088    
## Month_11      745.00     322.28   2.312 0.025044 *  
## Month_12      -89.07     167.22  -0.533 0.596675    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7594 on 49 degrees of freedom
## Multiple R-squared:  0.4863, Adjusted R-squared:  0.3605 
## F-statistic: 3.865 on 12 and 49 DF,  p-value: 0.0003605

## [1] "Results for crop: Wheat"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12660.6  -3094.1    227.8   2645.3  12989.5 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 19311.52     958.64  20.145   <2e-16 ***
## Month_1       -32.93     105.94  -0.311   0.7572    
## Month_2       -66.48     121.76  -0.546   0.5876    
## Month_3       142.65      94.09   1.516   0.1359    
## Month_4        93.74      72.84   1.287   0.2042    
## Month_5       110.53      61.25   1.805   0.0773 .  
## Month_6        49.61      38.45   1.290   0.2030    
## Month_7        68.37      57.34   1.192   0.2388    
## Month_8       370.36     141.18   2.623   0.0116 *  
## Month_9       246.75     198.49   1.243   0.2197    
## Month_10     -445.42     349.28  -1.275   0.2082    
## Month_11     -217.27     242.10  -0.897   0.3739    
## Month_12      102.03     125.61   0.812   0.4206    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5705 on 49 degrees of freedom
## Multiple R-squared:  0.3678, Adjusted R-squared:  0.213 
## F-statistic: 2.376 on 12 and 49 DF,  p-value: 0.01669

## [1] "Results for crop: Wine"
## [1] "No data available for this crop."
## [1] "Results for crop: Blueberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11931.2  -3416.9     40.2   2467.4  17139.0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 27981.612   1068.006  26.200   <2e-16 ***
## Month_1      -134.482    118.031  -1.139   0.2601    
## Month_2      -140.207    135.650  -1.034   0.3064    
## Month_3       128.518    104.827   1.226   0.2261    
## Month_4        -4.936     81.148  -0.061   0.9517    
## Month_5        63.461     68.235   0.930   0.3569    
## Month_6        24.973     42.841   0.583   0.5626    
## Month_7       -11.757     63.879  -0.184   0.8547    
## Month_8       274.108    157.282   1.743   0.0876 .  
## Month_9        95.019    221.137   0.430   0.6693    
## Month_10      177.198    389.125   0.455   0.6509    
## Month_11      114.656    269.721   0.425   0.6726    
## Month_12       84.667    139.945   0.605   0.5480    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6355 on 49 degrees of freedom
## Multiple R-squared:  0.1769, Adjusted R-squared:  -0.02462 
## F-statistic: 0.8778 on 12 and 49 DF,  p-value: 0.5741

## [1] "Results for crop: Grapes"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -31537  -7471   2092   9153  29061 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 67700.54    2429.09  27.871  < 2e-16 ***
## Month_1      -125.34     268.45  -0.467  0.64264    
## Month_2       -66.19     308.52  -0.215  0.83103    
## Month_3       302.17     238.42   1.267  0.21101    
## Month_4       286.12     184.56   1.550  0.12752    
## Month_5        52.63     155.20   0.339  0.73598    
## Month_6         4.35      97.44   0.045  0.96458    
## Month_7       -46.82     145.29  -0.322  0.74861    
## Month_8       594.42     357.73   1.662  0.10296    
## Month_9      1551.98     502.96   3.086  0.00334 ** 
## Month_10    -1242.53     885.03  -1.404  0.16664    
## Month_11      264.68     613.46   0.431  0.66803    
## Month_12     -208.75     318.29  -0.656  0.51499    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14450 on 49 degrees of freedom
## Multiple R-squared:  0.3792, Adjusted R-squared:  0.2272 
## F-statistic: 2.495 on 12 and 49 DF,  p-value: 0.01222

## [1] "Results for crop: Raspberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17820.0  -6968.2   -405.4   5302.3  20617.2 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 43135.53    1619.08  26.642   <2e-16 ***
## Month_1       -44.55     178.93  -0.249   0.8044    
## Month_2        47.11     205.64   0.229   0.8198    
## Month_3       207.05     158.92   1.303   0.1987    
## Month_4        98.80     123.02   0.803   0.4258    
## Month_5       130.06     103.44   1.257   0.2146    
## Month_6        10.29      64.95   0.159   0.8747    
## Month_7        11.01      96.84   0.114   0.9099    
## Month_8       263.14     238.44   1.104   0.2752    
## Month_9       599.14     335.24   1.787   0.0801 .  
## Month_10     -107.63     589.91  -0.182   0.8560    
## Month_11     -349.29     408.89  -0.854   0.3971    
## Month_12       15.50     212.15   0.073   0.9421    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9635 on 49 degrees of freedom
## Multiple R-squared:  0.1837, Adjusted R-squared:  -0.01622 
## F-statistic: 0.9188 on 12 and 49 DF,  p-value: 0.5357

## [1] "Results for crop: Strawberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -27165  -9654    295   7836  37911 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 42305.30    2599.62  16.274  < 2e-16 ***
## Month_1        92.96     287.30   0.324  0.74764    
## Month_2      -190.45     330.18  -0.577  0.56672    
## Month_3       570.16     255.16   2.235  0.03004 *  
## Month_4       244.04     197.52   1.236  0.22253    
## Month_5       198.78     166.09   1.197  0.23713    
## Month_6       202.15     104.28   1.939  0.05833 .  
## Month_7       239.27     155.49   1.539  0.13027    
## Month_8       803.04     382.84   2.098  0.04112 *  
## Month_9      1796.15     538.27   3.337  0.00162 ** 
## Month_10    -1200.40     947.16  -1.267  0.21102    
## Month_11       74.77     656.52   0.114  0.90979    
## Month_12      263.92     340.64   0.775  0.44219    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15470 on 49 degrees of freedom
## Multiple R-squared:  0.5022, Adjusted R-squared:  0.3803 
## F-statistic:  4.12 on 12 and 49 DF,  p-value: 0.0001929

Kelowna monthly

## [1] "There are 7  NA in the matrix X in Kelowna station"
## [1] "Results for crop: Apples"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -97125 -39427  -8252  36192  95814 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 166461.3     9204.8  18.084   <2e-16 ***
## Month_1       -584.5     1010.2  -0.579    0.566    
## Month_2      -1794.7     1137.4  -1.578    0.121    
## Month_3       1236.4      917.6   1.347    0.184    
## Month_4       -183.6     1047.9  -0.175    0.862    
## Month_5        654.3      617.5   1.060    0.295    
## Month_6        441.2      569.6   0.775    0.442    
## Month_7        235.5     1024.4   0.230    0.819    
## Month_8       5077.8     3179.3   1.597    0.117    
## Month_9       6652.6     3152.1   2.111    0.040 *  
## Month_10     -3069.9     3334.4  -0.921    0.362    
## Month_11       418.4     1427.3   0.293    0.771    
## Month_12      -453.8      994.0  -0.457    0.650    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 53640 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3101, Adjusted R-squared:  0.1377 
## F-statistic: 1.798 on 12 and 48 DF,  p-value: 0.0755

## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11224.6  -3693.7    300.7   3662.8  11372.9 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26403.302    948.583  27.834   <2e-16 ***
## Month_1      -200.357    104.108  -1.925   0.0602 .  
## Month_2      -223.187    117.207  -1.904   0.0629 .  
## Month_3       174.251     94.562   1.843   0.0716 .  
## Month_4       -38.479    107.990  -0.356   0.7232    
## Month_5       112.974     63.637   1.775   0.0822 .  
## Month_6         1.435     58.699   0.024   0.9806    
## Month_7       -28.891    105.571  -0.274   0.7855    
## Month_8       669.822    327.639   2.044   0.0464 *  
## Month_9       433.011    324.832   1.333   0.1888    
## Month_10     -215.774    343.622  -0.628   0.5330    
## Month_11      -84.118    147.083  -0.572   0.5701    
## Month_12      -12.822    102.433  -0.125   0.9009    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5528 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3804, Adjusted R-squared:  0.2255 
## F-statistic: 2.455 on 12 and 48 DF,  p-value: 0.01382

## [1] "Results for crop: Maize (corn)"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -31413 -12170  -2894   9570  31001 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 68017.23    2788.52  24.392   <2e-16 ***
## Month_1      -367.68     306.04  -1.201   0.2355    
## Month_2      -626.16     344.55  -1.817   0.0754 .  
## Month_3       559.18     277.98   2.012   0.0499 *  
## Month_4      -273.11     317.45  -0.860   0.3939    
## Month_5       144.34     187.07   0.772   0.4442    
## Month_6       168.89     172.56   0.979   0.3326    
## Month_7       350.62     310.34   1.130   0.2642    
## Month_8      1469.29     963.15   1.526   0.1337    
## Month_9      1916.71     954.90   2.007   0.0504 .  
## Month_10     -343.77    1010.13  -0.340   0.7351    
## Month_11       73.32     432.38   0.170   0.8661    
## Month_12       78.94     301.12   0.262   0.7943    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16250 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3577, Adjusted R-squared:  0.1971 
## F-statistic: 2.227 on 12 and 48 DF,  p-value: 0.02505

## [1] "Results for crop: Peaches and nectarines"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -14153  -5490  -1262   5376  22646 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 94200.881   1622.263  58.068   <2e-16 ***
## Month_1        54.751    178.045   0.308    0.760    
## Month_2      -165.778    200.447  -0.827    0.412    
## Month_3       157.115    161.720   0.972    0.336    
## Month_4        78.341    184.683   0.424    0.673    
## Month_5         4.315    108.833   0.040    0.969    
## Month_6      -113.398    100.387  -1.130    0.264    
## Month_7       208.230    180.547   1.153    0.254    
## Month_8       574.145    560.326   1.025    0.311    
## Month_9       182.230    555.527   0.328    0.744    
## Month_10     -940.413    587.661  -1.600    0.116    
## Month_11      347.187    251.542   1.380    0.174    
## Month_12     -104.652    175.180  -0.597    0.553    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9453 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2174, Adjusted R-squared:  0.02179 
## F-statistic: 1.111 on 12 and 48 DF,  p-value: 0.3733

## [1] "Results for crop: Wheat"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11209.8  -3796.6   -377.4   3152.6  15151.7 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 21464.44    1038.86  20.662   <2e-16 ***
## Month_1      -140.29     114.02  -1.230   0.2245    
## Month_2      -213.08     128.36  -1.660   0.1034    
## Month_3       180.26     103.56   1.741   0.0882 .  
## Month_4      -174.68     118.27  -1.477   0.1462    
## Month_5        54.74      69.69   0.785   0.4361    
## Month_6        54.42      64.29   0.847   0.4015    
## Month_7       -18.26     115.62  -0.158   0.8752    
## Month_8       512.06     358.82   1.427   0.1600    
## Month_9       610.33     355.75   1.716   0.0927 .  
## Month_10     -258.02     376.32  -0.686   0.4962    
## Month_11      -74.84     161.08  -0.465   0.6443    
## Month_12      -17.72     112.18  -0.158   0.8751    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6054 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3007, Adjusted R-squared:  0.1259 
## F-statistic:  1.72 on 12 and 48 DF,  p-value: 0.09187

## [1] "Results for crop: Wine"
## [1] "No data available for this crop."
## [1] "Results for crop: Blueberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13918.5  -3792.6   -126.6   1995.7  16782.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29365.79    1131.82  25.946   <2e-16 ***
## Month_1      -107.59     124.22  -0.866    0.391    
## Month_2       -88.39     139.85  -0.632    0.530    
## Month_3        83.94     112.83   0.744    0.461    
## Month_4        48.09     128.85   0.373    0.711    
## Month_5       -77.52      75.93  -1.021    0.312    
## Month_6       112.42      70.04   1.605    0.115    
## Month_7       -23.60     125.96  -0.187    0.852    
## Month_8       236.91     390.93   0.606    0.547    
## Month_9       166.80     387.58   0.430    0.669    
## Month_10      172.64     410.00   0.421    0.676    
## Month_11     -107.09     175.50  -0.610    0.545    
## Month_12       28.53     122.22   0.233    0.816    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6595 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1298, Adjusted R-squared:  -0.08779 
## F-statistic: 0.5965 on 12 and 48 DF,  p-value: 0.8341

## [1] "Results for crop: Grapes"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37202  -7839   1099   8652  23218 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 71851.85    2561.83  28.047   <2e-16 ***
## Month_1      -405.20     281.16  -1.441   0.1560    
## Month_2      -577.77     316.54  -1.825   0.0742 .  
## Month_3        14.96     255.38   0.059   0.9535    
## Month_4       344.61     291.65   1.182   0.2432    
## Month_5       341.17     171.87   1.985   0.0529 .  
## Month_6        25.43     158.53   0.160   0.8732    
## Month_7      -184.93     285.11  -0.649   0.5197    
## Month_8      2262.38     884.85   2.557   0.0138 *  
## Month_9       899.62     877.27   1.025   0.3103    
## Month_10     -255.89     928.02  -0.276   0.7839    
## Month_11      410.47     397.23   1.033   0.3066    
## Month_12       42.58     276.64   0.154   0.8783    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14930 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3514, Adjusted R-squared:  0.1892 
## F-statistic: 2.167 on 12 and 48 DF,  p-value: 0.02931

## [1] "Results for crop: Raspberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14682.6  -7423.7   -193.1   6081.1  18921.7 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 45912.01    1489.48  30.824  < 2e-16 ***
## Month_1      -162.22     163.47  -0.992  0.32600    
## Month_2      -150.52     184.04  -0.818  0.41748    
## Month_3       162.41     148.48   1.094  0.27950    
## Month_4       350.11     169.57   2.065  0.04437 *  
## Month_5       269.45      99.92   2.697  0.00963 ** 
## Month_6       -36.11      92.17  -0.392  0.69699    
## Month_7       -17.82     165.77  -0.107  0.91485    
## Month_8       224.31     514.46   0.436  0.66479    
## Month_9       463.59     510.06   0.909  0.36794    
## Month_10      546.11     539.56   1.012  0.31655    
## Month_11       42.36     230.95   0.183  0.85524    
## Month_12     -178.26     160.84  -1.108  0.27326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8679 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.335,  Adjusted R-squared:  0.1687 
## F-statistic: 2.015 on 12 and 48 DF,  p-value: 0.04345

## [1] "Results for crop: Strawberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -36558  -7983   -761   7877  32691 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 50249.40    2897.35  17.343   <2e-16 ***
## Month_1      -353.10     317.99  -1.110   0.2723    
## Month_2      -753.93     358.00  -2.106   0.0405 *  
## Month_3       344.17     288.83   1.192   0.2393    
## Month_4       -26.56     329.84  -0.081   0.9362    
## Month_5       393.99     194.37   2.027   0.0482 *  
## Month_6       137.54     179.29   0.767   0.4468    
## Month_7       183.98     322.45   0.571   0.5710    
## Month_8      2159.48    1000.74   2.158   0.0360 *  
## Month_9      2334.12     992.17   2.353   0.0228 *  
## Month_10     -803.95    1049.56  -0.766   0.4474    
## Month_11      321.73     449.25   0.716   0.4774    
## Month_12      190.80     312.87   0.610   0.5448    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16880 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.4155, Adjusted R-squared:  0.2694 
## F-statistic: 2.844 on 12 and 48 DF,  p-value: 0.005024

FortStJohn monthly

## [1] "There are 5  NA in the matrix X in FortStJoh station"
## [1] "Results for crop: Apples"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -79652 -43998 -14534  46262 110352 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 164908.19    9397.31  17.548   <2e-16 ***
## Month_1         73.09     263.57   0.277    0.783    
## Month_2        206.15     477.12   0.432    0.668    
## Month_3         14.85     616.14   0.024    0.981    
## Month_4        443.26     507.29   0.874    0.386    
## Month_5        -83.92     693.65  -0.121    0.904    
## Month_6        648.67     503.84   1.287    0.204    
## Month_7        565.91    1053.46   0.537    0.594    
## Month_8        813.54    2055.87   0.396    0.694    
## Month_9       1399.99    1566.74   0.894    0.376    
## Month_10     -2174.33    1898.68  -1.145    0.258    
## Month_11       -48.21     768.93  -0.063    0.950    
## Month_12       207.48     371.07   0.559    0.579    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 60380 on 49 degrees of freedom
## Multiple R-squared:  0.1261, Adjusted R-squared:  -0.08794 
## F-statistic: 0.5891 on 12 and 49 DF,  p-value: 0.8403

## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10531.8  -4254.7   -618.8   3653.1  13275.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 27029.7410  1006.0030  26.868   <2e-16 ***
## Month_1        -0.4965    28.2157  -0.018    0.986    
## Month_2       -20.3518    51.0763  -0.398    0.692    
## Month_3        77.6953    65.9595   1.178    0.245    
## Month_4        87.1528    54.3066   1.605    0.115    
## Month_5       -48.5952    74.2567  -0.654    0.516    
## Month_6        -7.8296    53.9371  -0.145    0.885    
## Month_7        28.8305   112.7754   0.256    0.799    
## Month_8       -48.1397   220.0858  -0.219    0.828    
## Month_9       185.4445   167.7225   1.106    0.274    
## Month_10      -75.7669   203.2578  -0.373    0.711    
## Month_11      -59.2748    82.3161  -0.720    0.475    
## Month_12       27.4565    39.7243   0.691    0.493    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6463 on 49 degrees of freedom
## Multiple R-squared:  0.1371, Adjusted R-squared:  -0.07417 
## F-statistic: 0.649 on 12 and 49 DF,  p-value: 0.7898

## [1] "Results for crop: Maize (corn)"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -24767 -13776  -3217  12260  33190 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 67543.57    2813.95  24.003   <2e-16 ***
## Month_1        31.62      78.92   0.401   0.6905    
## Month_2       -29.08     142.87  -0.204   0.8395    
## Month_3       -14.36     184.50  -0.078   0.9383    
## Month_4       277.38     151.90   1.826   0.0739 .  
## Month_5       -23.04     207.71  -0.111   0.9121    
## Month_6       264.73     150.87   1.755   0.0856 .  
## Month_7       355.82     315.45   1.128   0.2648    
## Month_8       339.49     615.62   0.551   0.5838    
## Month_9       394.50     469.15   0.841   0.4045    
## Month_10     -544.73     568.54  -0.958   0.3427    
## Month_11      -55.79     230.25  -0.242   0.8096    
## Month_12       26.34     111.12   0.237   0.8136    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18080 on 49 degrees of freedom
## Multiple R-squared:  0.194,  Adjusted R-squared:  -0.003383 
## F-statistic: 0.9829 on 12 and 49 DF,  p-value: 0.478

## [1] "Results for crop: Peaches and nectarines"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -18939  -5971  -1971   7355  19588 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 93855.265   1543.634  60.801   <2e-16 ***
## Month_1       -22.834     43.295  -0.527    0.600    
## Month_2       -14.105     78.373  -0.180    0.858    
## Month_3        92.711    101.210   0.916    0.364    
## Month_4       -66.526     83.329  -0.798    0.429    
## Month_5       -72.265    113.941  -0.634    0.529    
## Month_6        62.739     82.762   0.758    0.452    
## Month_7       130.013    173.045   0.751    0.456    
## Month_8      -312.848    337.705  -0.926    0.359    
## Month_9        53.473    257.357   0.208    0.836    
## Month_10     -395.751    311.883  -1.269    0.210    
## Month_11        3.651    126.308   0.029    0.977    
## Month_12      -44.120     60.954  -0.724    0.473    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9918 on 49 degrees of freedom
## Multiple R-squared:  0.1238, Adjusted R-squared:  -0.09081 
## F-statistic: 0.5768 on 12 and 49 DF,  p-value: 0.85

## [1] "Results for crop: Wheat"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -9887  -4263  -1435   3835  15253 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 21688.005   1057.300  20.513   <2e-16 ***
## Month_1         6.936     29.654   0.234    0.816    
## Month_2       -35.909     53.681  -0.669    0.507    
## Month_3        46.301     69.323   0.668    0.507    
## Month_4        83.593     57.076   1.465    0.149    
## Month_5       -24.932     78.043  -0.319    0.751    
## Month_6        30.518     56.687   0.538    0.593    
## Month_7        39.670    118.526   0.335    0.739    
## Month_8        32.964    231.308   0.143    0.887    
## Month_9       141.447    176.275   0.802    0.426    
## Month_10     -163.093    213.622  -0.763    0.449    
## Month_11      -49.570     86.513  -0.573    0.569    
## Month_12        5.235     41.750   0.125    0.901    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6793 on 49 degrees of freedom
## Multiple R-squared:  0.1036, Adjusted R-squared:  -0.116 
## F-statistic: 0.4718 on 12 and 49 DF,  p-value: 0.9216

## [1] "Results for crop: Wine"
## [1] "No data available for this crop."
## [1] "Results for crop: Blueberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -11684  -3339   -533   1686  17547 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28864.199   1064.988  27.103   <2e-16 ***
## Month_1        13.427     29.870   0.450    0.655    
## Month_2       -23.007     54.071  -0.425    0.672    
## Month_3        44.791     69.827   0.641    0.524    
## Month_4        13.052     57.491   0.227    0.821    
## Month_5        46.588     78.611   0.593    0.556    
## Month_6        40.467     57.100   0.709    0.482    
## Month_7        34.767    119.388   0.291    0.772    
## Month_8       -55.753    232.990  -0.239    0.812    
## Month_9        71.991    177.557   0.405    0.687    
## Month_10      -25.244    215.175  -0.117    0.907    
## Month_11       -4.372     87.142  -0.050    0.960    
## Month_12      -16.184     42.053  -0.385    0.702    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6842 on 49 degrees of freedom
## Multiple R-squared:  0.04597,    Adjusted R-squared:  -0.1877 
## F-statistic: 0.1967 on 12 and 49 DF,  p-value: 0.998

## [1] "Results for crop: Grapes"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33266  -7118   1448   8605  33226 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 71250.67    2506.95  28.421   <2e-16 ***
## Month_1      -125.53      70.31  -1.785   0.0804 .  
## Month_2        65.39     127.28   0.514   0.6097    
## Month_3        84.00     164.37   0.511   0.6116    
## Month_4       165.39     135.33   1.222   0.2275    
## Month_5      -170.25     185.05  -0.920   0.3621    
## Month_6       113.98     134.41   0.848   0.4006    
## Month_7       -62.84     281.04  -0.224   0.8240    
## Month_8       259.13     548.45   0.472   0.6387    
## Month_9       200.94     417.96   0.481   0.6328    
## Month_10     -502.21     506.52  -0.991   0.3263    
## Month_11     -373.49     205.13  -1.821   0.0748 .  
## Month_12      111.24      98.99   1.124   0.2666    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16110 on 49 degrees of freedom
## Multiple R-squared:  0.2292, Adjusted R-squared:  0.04047 
## F-statistic: 1.214 on 12 and 49 DF,  p-value: 0.3005

## [1] "Results for crop: Raspberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17783.7  -7321.2   -279.2   6605.6  19913.0 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 45849.4139  1569.5933  29.211   <2e-16 ***
## Month_1        35.1143    44.0228   0.798    0.429    
## Month_2        53.1092    79.6906   0.666    0.508    
## Month_3        -9.9611   102.9117  -0.097    0.923    
## Month_4        77.6044    84.7307   0.916    0.364    
## Month_5        36.2146   115.8573   0.313    0.756    
## Month_6         0.6290    84.1541   0.007    0.994    
## Month_7        40.4917   175.9553   0.230    0.819    
## Month_8        85.6888   343.3838   0.250    0.804    
## Month_9       157.5987   261.6852   0.602    0.550    
## Month_10        0.9095   317.1284   0.003    0.998    
## Month_11      -60.4780   128.4318  -0.471    0.640    
## Month_12       82.5281    61.9789   1.332    0.189    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10080 on 49 degrees of freedom
## Multiple R-squared:  0.1057, Adjusted R-squared:  -0.1133 
## F-statistic: 0.4826 on 12 and 49 DF,  p-value: 0.9153

## [1] "Results for crop: Strawberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34295 -13879  -1044   9909  44912 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 51035.224   3185.986  16.019   <2e-16 ***
## Month_1        -4.289     89.358  -0.048   0.9619    
## Month_2       107.102    161.757   0.662   0.5110    
## Month_3       131.357    208.892   0.629   0.5324    
## Month_4       120.942    171.988   0.703   0.4853    
## Month_5       -75.278    235.169  -0.320   0.7503    
## Month_6       340.313    170.817   1.992   0.0519 .  
## Month_7        14.794    357.157   0.041   0.9671    
## Month_8       -64.264    697.006  -0.092   0.9269    
## Month_9       546.313    531.173   1.029   0.3088    
## Month_10     -373.816    643.712  -0.581   0.5641    
## Month_11      -62.687    260.693  -0.240   0.8110    
## Month_12       50.618    125.806   0.402   0.6892    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20470 on 49 degrees of freedom
## Multiple R-squared:  0.1284, Adjusted R-squared:  -0.08503 
## F-statistic: 0.6016 on 12 and 49 DF,  p-value: 0.8301

linear reg for yield VS weekly Max Temp

Abbotsford

## [1] "Results for crop: Apples"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -27887.8  -8791.8   -970.2   9462.8  30817.2 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -379410.47  216422.90  -1.753   0.1177  
## Week_1         5569.15    3867.69   1.440   0.1879  
## Week_2         4633.31    4580.18   1.012   0.3413  
## Week_3        -3710.64    4951.29  -0.749   0.4751  
## Week_4           27.95    4122.76   0.007   0.9948  
## Week_5          487.06    4704.67   0.104   0.9201  
## Week_6        -1109.62    6349.16  -0.175   0.8656  
## Week_7         2491.88    5178.18   0.481   0.6432  
## Week_8         1956.54    8225.02   0.238   0.8180  
## Week_9        -2324.53    4514.65  -0.515   0.6206  
## Week_10       -3040.07    7166.04  -0.424   0.6826  
## Week_11      -15492.00   11097.32  -1.396   0.2002  
## Week_12        6499.98    7199.61   0.903   0.3930  
## Week_13        8247.42   11298.69   0.730   0.4862  
## Week_14       -9458.74   16998.02  -0.556   0.5931  
## Week_15        8600.07   10328.70   0.833   0.4292  
## Week_16       -1342.51    6477.99  -0.207   0.8410  
## Week_17       11582.98    7095.79   1.632   0.1412  
## Week_18        6484.74    7605.25   0.853   0.4186  
## Week_19        1256.56    6470.79   0.194   0.8509  
## Week_20        7659.30    7092.90   1.080   0.3117  
## Week_21        2722.31    7773.04   0.350   0.7352  
## Week_22       -4391.71    7022.84  -0.625   0.5492  
## Week_23         181.00    6893.98   0.026   0.9797  
## Week_24       -7746.16    6029.18  -1.285   0.2348  
## Week_25        2684.34    7933.91   0.338   0.7438  
## Week_26        1207.84    7960.36   0.152   0.8832  
## Week_27       10662.66   11377.60   0.937   0.3761  
## Week_28        3922.51    9126.18   0.430   0.6787  
## Week_29      -12120.23    9269.33  -1.308   0.2273  
## Week_30        8497.35    8158.22   1.042   0.3281  
## Week_31       10183.10    7471.30   1.363   0.2100  
## Week_32       -6251.69    4441.55  -1.408   0.1969  
## Week_33      -12660.54    8296.48  -1.526   0.1655  
## Week_34       24884.44    8528.16   2.918   0.0194 *
## Week_35       -4741.30    5859.62  -0.809   0.4418  
## Week_36        3778.39    7869.29   0.480   0.6440  
## Week_37        2959.59    9376.17   0.316   0.7603  
## Week_38         477.54    8461.64   0.056   0.9564  
## Week_39      -11950.24    8350.59  -1.431   0.1903  
## Week_40        6703.27    7324.00   0.915   0.3868  
## Week_41      -10685.91    8618.50  -1.240   0.2502  
## Week_42        8432.17    8595.78   0.981   0.3553  
## Week_43        1782.38    6710.56   0.266   0.7973  
## Week_44       -4602.96    4994.26  -0.922   0.3837  
## Week_45      -10205.91    7467.38  -1.367   0.2089  
## Week_46        9118.57    6963.67   1.309   0.2267  
## Week_47       -2333.68    4243.52  -0.550   0.5974  
## Week_48         751.76    7298.11   0.103   0.9205  
## Week_49         697.45    3835.69   0.182   0.8602  
## Week_50       -2093.30    4034.65  -0.519   0.6179  
## Week_51       -4463.51    4658.36  -0.958   0.3660  
## Week_52        6020.65    6413.27   0.939   0.3753  
## Week_53       -1509.30    3352.33  -0.450   0.6645  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39000 on 8 degrees of freedom
## Multiple R-squared:  0.9405, Adjusted R-squared:  0.5459 
## F-statistic: 2.384 on 53 and 8 DF,  p-value: 0.09579

## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4204.1 -1123.1   117.2  1306.2  3614.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -13226.592  28152.253  -0.470   0.6510  
## Week_1        -206.202    503.109  -0.410   0.6927  
## Week_2         471.703    595.789   0.792   0.4514  
## Week_3        -430.418    644.063  -0.668   0.5228  
## Week_4         460.727    536.288   0.859   0.4153  
## Week_5        -353.637    611.982  -0.578   0.5793  
## Week_6         861.448    825.897   1.043   0.3274  
## Week_7         267.191    673.576   0.397   0.7020  
## Week_8        -383.405   1069.908  -0.358   0.7294  
## Week_9         484.765    587.265   0.825   0.4330  
## Week_10      -2000.691    932.157  -2.146   0.0641 .
## Week_11       -971.361   1443.538  -0.673   0.5200  
## Week_12       1229.279    936.524   1.313   0.2257  
## Week_13       -672.262   1469.731  -0.457   0.6595  
## Week_14       -642.650   2211.100  -0.291   0.7787  
## Week_15       2896.384   1343.555   2.156   0.0632 .
## Week_16       -314.541    842.656  -0.373   0.7186  
## Week_17       1648.437    923.019   1.786   0.1119  
## Week_18        755.261    989.290   0.763   0.4671  
## Week_19       1460.848    841.719   1.736   0.1209  
## Week_20        550.243    922.643   0.596   0.5674  
## Week_21         -2.594   1011.116  -0.003   0.9980  
## Week_22        959.620    913.529   1.050   0.3242  
## Week_23       -113.083    896.768  -0.126   0.9028  
## Week_24      -1104.231    784.275  -1.408   0.1968  
## Week_25       -914.571   1032.042  -0.886   0.4014  
## Week_26        772.857   1035.482   0.746   0.4768  
## Week_27       1194.739   1479.997   0.807   0.4429  
## Week_28       -243.264   1187.132  -0.205   0.8428  
## Week_29      -2210.946   1205.753  -1.834   0.1041  
## Week_30        265.191   1061.220   0.250   0.8090  
## Week_31        226.036    971.866   0.233   0.8219  
## Week_32        515.944    577.756   0.893   0.3979  
## Week_33       -795.922   1079.205  -0.738   0.4819  
## Week_34       3514.440   1109.341   3.168   0.0132 *
## Week_35       -396.296    762.219  -0.520   0.6172  
## Week_36      -1025.491   1023.636  -1.002   0.3458  
## Week_37       -345.374   1219.650  -0.283   0.7842  
## Week_38       1584.012   1100.689   1.439   0.1881  
## Week_39      -1461.904   1086.243  -1.346   0.2152  
## Week_40        778.879    952.705   0.818   0.4373  
## Week_41      -1850.641   1121.093  -1.651   0.1374  
## Week_42         -3.340   1118.138  -0.003   0.9977  
## Week_43         40.480    872.908   0.046   0.9641  
## Week_44       -631.555    649.653  -0.972   0.3595  
## Week_45      -2162.071    971.355  -2.226   0.0567 .
## Week_46       1061.800    905.833   1.172   0.2748  
## Week_47       -344.553    551.996  -0.624   0.5499  
## Week_48       -614.060    949.336  -0.647   0.5359  
## Week_49         90.030    498.946   0.180   0.8613  
## Week_50        125.258    524.826   0.239   0.8174  
## Week_51        -41.222    605.959  -0.068   0.9474  
## Week_52        634.156    834.237   0.760   0.4690  
## Week_53       -183.356    436.071  -0.420   0.6852  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5074 on 8 degrees of freedom
## Multiple R-squared:  0.9132, Adjusted R-squared:  0.3381 
## F-statistic: 1.588 on 53 and 8 DF,  p-value: 0.2509

## [1] "Results for crop: Maize (corn)"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8212.3 -3126.3  -288.8  2799.6  7937.3 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -95390.10   59691.64  -1.598   0.1487  
## Week_1         750.61    1066.75   0.704   0.5016  
## Week_2        1107.53    1263.26   0.877   0.4062  
## Week_3       -1040.10    1365.62  -0.762   0.4681  
## Week_4        1237.90    1137.10   1.089   0.3080  
## Week_5       -1064.53    1297.60  -0.820   0.4358  
## Week_6         594.68    1751.16   0.340   0.7429  
## Week_7         530.56    1428.19   0.371   0.7199  
## Week_8         366.48    2268.54   0.162   0.8757  
## Week_9        -624.03    1245.19  -0.501   0.6298  
## Week_10      -2899.75    1976.47  -1.467   0.1805  
## Week_11      -4937.88    3060.75  -1.613   0.1453  
## Week_12       1578.46    1985.73   0.795   0.4496  
## Week_13       2252.13    3116.29   0.723   0.4905  
## Week_14      -2017.69    4688.23  -0.430   0.6783  
## Week_15       2250.45    2848.76   0.790   0.4523  
## Week_16        447.49    1786.70   0.250   0.8085  
## Week_17       4306.02    1957.09   2.200   0.0590 .
## Week_18       1908.14    2097.61   0.910   0.3896  
## Week_19        991.73    1784.71   0.556   0.5936  
## Week_20       1918.57    1956.29   0.981   0.3555  
## Week_21        -42.95    2143.88  -0.020   0.9845  
## Week_22        168.93    1936.97   0.087   0.9326  
## Week_23       -510.02    1901.43  -0.268   0.7953  
## Week_24      -2308.19    1662.91  -1.388   0.2026  
## Week_25       -978.05    2188.25  -0.447   0.6668  
## Week_26       1289.66    2195.55   0.587   0.5731  
## Week_27       4283.96    3138.06   1.365   0.2094  
## Week_28       1816.04    2517.09   0.721   0.4912  
## Week_29      -3301.41    2556.58  -1.291   0.2326  
## Week_30       2132.15    2250.12   0.948   0.3711  
## Week_31       1333.15    2060.66   0.647   0.5358  
## Week_32       -970.70    1225.02  -0.792   0.4510  
## Week_33      -2056.35    2288.25  -0.899   0.3951  
## Week_34       7513.20    2352.15   3.194   0.0127 *
## Week_35      -2363.57    1616.14  -1.462   0.1818  
## Week_36       1555.51    2170.43   0.717   0.4940  
## Week_37      -1101.11    2586.04  -0.426   0.6815  
## Week_38       1467.94    2333.81   0.629   0.5469  
## Week_39      -4355.88    2303.18  -1.891   0.0952 .
## Week_40       1979.71    2020.03   0.980   0.3558  
## Week_41      -2932.57    2377.07  -1.234   0.2523  
## Week_42       2021.47    2370.80   0.853   0.4186  
## Week_43       -137.38    1850.84  -0.074   0.9427  
## Week_44       -533.27    1377.47  -0.387   0.7087  
## Week_45      -4128.94    2059.58  -2.005   0.0799 .
## Week_46       3252.47    1920.65   1.693   0.1288  
## Week_47       -977.19    1170.40  -0.835   0.4280  
## Week_48         55.03    2012.89   0.027   0.9789  
## Week_49        -99.20    1057.92  -0.094   0.9276  
## Week_50        303.35    1112.80   0.273   0.7921  
## Week_51      -1293.32    1284.82  -1.007   0.3436  
## Week_52       2003.27    1768.85   1.133   0.2902  
## Week_53      -1053.95     924.61  -1.140   0.2873  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10760 on 8 degrees of freedom
## Multiple R-squared:  0.9534, Adjusted R-squared:  0.6447 
## F-statistic: 3.089 on 53 and 8 DF,  p-value: 0.04663

## [1] "Results for crop: Peaches and nectarines"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5074.5 -1479.5    83.5  1702.3  5149.9 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -2186.260  31963.754  -0.068   0.9471  
## Week_1        934.411    571.224   1.636   0.1405  
## Week_2         -5.987    676.452  -0.009   0.9932  
## Week_3       -303.844    731.262  -0.416   0.6887  
## Week_4       -341.289    608.895  -0.561   0.5905  
## Week_5        -19.539    694.838  -0.028   0.9783  
## Week_6       -437.527    937.715  -0.467   0.6532  
## Week_7       -468.809    764.771  -0.613   0.5569  
## Week_8       -149.493   1214.762  -0.123   0.9051  
## Week_9         -3.953    666.774  -0.006   0.9954  
## Week_10      -192.018   1058.361  -0.181   0.8605  
## Week_11      -120.229   1638.977  -0.073   0.9433  
## Week_12       158.598   1063.319   0.149   0.8851  
## Week_13       704.678   1668.717   0.422   0.6839  
## Week_14      2715.992   2510.458   1.082   0.3108  
## Week_15      -328.866   1525.457  -0.216   0.8347  
## Week_16       119.674    956.743   0.125   0.9035  
## Week_17      1498.923   1047.986   1.430   0.1905  
## Week_18     -1482.445   1123.229  -1.320   0.2234  
## Week_19      -465.128    955.679  -0.487   0.6395  
## Week_20       943.113   1047.558   0.900   0.3943  
## Week_21      -148.495   1148.010  -0.129   0.9003  
## Week_22      -286.892   1037.211  -0.277   0.7891  
## Week_23       638.609   1018.180   0.627   0.5480  
## Week_24      -992.207    890.457  -1.114   0.2975  
## Week_25      1731.828   1171.769   1.478   0.1777  
## Week_26     -1720.659   1175.674  -1.464   0.1815  
## Week_27      2407.086   1680.372   1.432   0.1899  
## Week_28      1175.362   1347.856   0.872   0.4086  
## Week_29       245.021   1368.999   0.179   0.8624  
## Week_30       675.510   1204.897   0.561   0.5904  
## Week_31      1373.139   1103.446   1.244   0.2486  
## Week_32      -115.916    655.978  -0.177   0.8641  
## Week_33     -2464.103   1225.317  -2.011   0.0792 .
## Week_34       910.440   1259.533   0.723   0.4904  
## Week_35       -15.881    865.415  -0.018   0.9858  
## Week_36     -1366.769   1162.225  -1.176   0.2734  
## Week_37      -259.754   1384.777  -0.188   0.8559  
## Week_38       293.554   1249.710   0.235   0.8202  
## Week_39       -12.326   1233.308  -0.010   0.9923  
## Week_40      1340.438   1081.690   1.239   0.2504  
## Week_41      -266.946   1272.877  -0.210   0.8391  
## Week_42       840.961   1269.521   0.662   0.5263  
## Week_43      1257.420    991.090   1.269   0.2402  
## Week_44     -1471.160    737.609  -1.994   0.0812 .
## Week_45     -1050.131   1102.866  -0.952   0.3689  
## Week_46      2202.746   1028.472   2.142   0.0646 .
## Week_47      -198.656    626.730  -0.317   0.7594  
## Week_48      1264.609   1077.866   1.173   0.2744  
## Week_49       355.754    566.498   0.628   0.5475  
## Week_50       334.252    595.882   0.561   0.5902  
## Week_51      -546.703    687.999  -0.795   0.4498  
## Week_52       109.568    947.184   0.116   0.9108  
## Week_53       438.117    495.110   0.885   0.4020  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5761 on 8 degrees of freedom
## Multiple R-squared:  0.9517, Adjusted R-squared:  0.632 
## F-statistic: 2.976 on 53 and 8 DF,  p-value: 0.05191

## [1] "Results for crop: Wheat"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3638.5 -1138.7   -64.5  1057.1  3613.5 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -13620.06   26181.53  -0.520   0.6170  
## Week_1        -170.93     467.89  -0.365   0.7243  
## Week_2         387.58     554.08   0.699   0.5041  
## Week_3        -766.01     598.98  -1.279   0.2368  
## Week_4         566.51     498.75   1.136   0.2889  
## Week_5        -374.77     569.14  -0.658   0.5287  
## Week_6         494.10     768.08   0.643   0.5380  
## Week_7         359.59     626.42   0.574   0.5817  
## Week_8        -433.02     995.01  -0.435   0.6749  
## Week_9         361.24     546.16   0.661   0.5269  
## Week_10      -1921.02     866.90  -2.216   0.0575 .
## Week_11      -1680.93    1342.49  -1.252   0.2459  
## Week_12       1524.00     870.97   1.750   0.1183  
## Week_13        574.84    1366.85   0.421   0.6851  
## Week_14      -2024.14    2056.32  -0.984   0.3538  
## Week_15       2290.80    1249.50   1.833   0.1041  
## Week_16       -644.50     783.67  -0.822   0.4347  
## Week_17       1634.33     858.41   1.904   0.0934 .
## Week_18       1213.25     920.04   1.319   0.2238  
## Week_19        868.42     782.80   1.109   0.2995  
## Week_20        972.18     858.06   1.133   0.2900  
## Week_21       -585.35     940.34  -0.622   0.5509  
## Week_22        460.18     849.58   0.542   0.6028  
## Week_23         68.34     833.99   0.082   0.9367  
## Week_24      -1212.66     729.37  -1.663   0.1350  
## Week_25       -953.90     959.80  -0.994   0.3494  
## Week_26        929.03     963.00   0.965   0.3629  
## Week_27        794.17    1376.39   0.577   0.5798  
## Week_28        634.97    1104.03   0.575   0.5810  
## Week_29      -2294.68    1121.35  -2.046   0.0749 .
## Week_30        403.28     986.93   0.409   0.6935  
## Week_31        689.49     903.83   0.763   0.4674  
## Week_32        241.33     537.31   0.449   0.6652  
## Week_33       -738.99    1003.66  -0.736   0.4826  
## Week_34       3245.97    1031.68   3.146   0.0137 *
## Week_35       -597.21     708.86  -0.842   0.4240  
## Week_36       -692.89     951.98  -0.728   0.4875  
## Week_37       -349.95    1134.27  -0.309   0.7656  
## Week_38       1247.04    1023.64   1.218   0.2578  
## Week_39      -1373.30    1010.20  -1.359   0.2111  
## Week_40        106.14     886.01   0.120   0.9076  
## Week_41      -1294.95    1042.61  -1.242   0.2494  
## Week_42        530.63    1039.87   0.510   0.6236  
## Week_43        222.70     811.80   0.274   0.7908  
## Week_44        -66.02     604.18  -0.109   0.9157  
## Week_45      -2097.31     903.36  -2.322   0.0488 *
## Week_46       1781.16     842.42   2.114   0.0674 .
## Week_47       -235.84     513.35  -0.459   0.6582  
## Week_48       -616.75     882.88  -0.699   0.5046  
## Week_49       -352.65     464.02  -0.760   0.4691  
## Week_50       -146.35     488.09  -0.300   0.7719  
## Week_51        150.73     563.54   0.267   0.7959  
## Week_52        206.51     775.84   0.266   0.7968  
## Week_53       -115.02     405.54  -0.284   0.7839  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4719 on 8 degrees of freedom
## Multiple R-squared:  0.9294, Adjusted R-squared:  0.4615 
## F-statistic: 1.987 on 53 and 8 DF,  p-value: 0.1517

## [1] "Results for crop: Wine"
## [1] "No data available for this crop."
## [1] "Results for crop: Blueberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5846.3 -1290.8   -50.8  1531.9  4371.4 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  2614.52   33260.31   0.079   0.9393  
## Week_1       -677.65     594.40  -1.140   0.2872  
## Week_2       -459.24     703.89  -0.652   0.5324  
## Week_3       -386.17     760.92  -0.507   0.6255  
## Week_4       1072.94     633.59   1.693   0.1288  
## Week_5       -894.85     723.02  -1.238   0.2509  
## Week_6       -484.15     975.75  -0.496   0.6331  
## Week_7        745.73     795.79   0.937   0.3761  
## Week_8      -1011.55    1264.04  -0.800   0.4467  
## Week_9       -355.68     693.82  -0.513   0.6221  
## Week_10      -830.61    1101.29  -0.754   0.4723  
## Week_11       816.93    1705.46   0.479   0.6448  
## Week_12      1693.65    1106.45   1.531   0.1644  
## Week_13      -963.66    1736.41  -0.555   0.5941  
## Week_14      2934.10    2612.29   1.123   0.2939  
## Week_15      -766.97    1587.34  -0.483   0.6419  
## Week_16      1045.38     995.55   1.050   0.3244  
## Week_17      -227.59    1090.50  -0.209   0.8399  
## Week_18     -2037.50    1168.79  -1.743   0.1195  
## Week_19       358.32     994.44   0.360   0.7279  
## Week_20       377.12    1090.05   0.346   0.7383  
## Week_21     -1059.99    1194.58  -0.887   0.4008  
## Week_22     -1251.47    1079.28  -1.160   0.2797  
## Week_23        37.41    1059.48   0.035   0.9727  
## Week_24       706.22     926.58   0.762   0.4678  
## Week_25       801.80    1219.30   0.658   0.5293  
## Week_26       511.63    1223.36   0.418   0.6868  
## Week_27       -71.87    1748.53  -0.041   0.9682  
## Week_28      1418.16    1402.53   1.011   0.3415  
## Week_29       731.50    1424.53   0.514   0.6215  
## Week_30     -1858.69    1253.77  -1.482   0.1765  
## Week_31      -307.74    1148.21  -0.268   0.7955  
## Week_32        74.90     682.59   0.110   0.9153  
## Week_33      1348.41    1275.02   1.058   0.3211  
## Week_34       390.25    1310.62   0.298   0.7735  
## Week_35     -1038.43     900.52  -1.153   0.2821  
## Week_36      -347.90    1209.37  -0.288   0.7809  
## Week_37      -122.80    1440.95  -0.085   0.9342  
## Week_38       251.42    1300.40   0.193   0.8515  
## Week_39      -344.60    1283.34  -0.269   0.7951  
## Week_40      -416.53    1125.57  -0.370   0.7209  
## Week_41       476.62    1324.51   0.360   0.7283  
## Week_42      1964.90    1321.02   1.487   0.1752  
## Week_43     -1325.35    1031.29  -1.285   0.2347  
## Week_44      1780.50     767.53   2.320   0.0489 *
## Week_45     -1894.00    1147.60  -1.650   0.1375  
## Week_46       934.35    1070.19   0.873   0.4081  
## Week_47      1417.49     652.15   2.174   0.0615 .
## Week_48      -107.34    1121.59  -0.096   0.9261  
## Week_49      -844.77     589.48  -1.433   0.1897  
## Week_50      -468.68     620.05  -0.756   0.4714  
## Week_51       252.46     715.91   0.353   0.7335  
## Week_52     -1091.62     985.60  -1.108   0.3002  
## Week_53       177.79     515.19   0.345   0.7389  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5994 on 8 degrees of freedom
## Multiple R-squared:  0.8805, Adjusted R-squared:  0.08847 
## F-statistic: 1.112 on 53 and 8 DF,  p-value: 0.4763

## [1] "Results for crop: Grapes"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11813.4  -3030.8    397.2   3226.0  12447.3 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 93315.0390 74777.6071   1.248   0.2474  
## Week_1       -419.4469  1336.3506  -0.314   0.7616  
## Week_2      -1433.2573  1582.5262  -0.906   0.3916  
## Week_3       2258.2339  1710.7507   1.320   0.2233  
## Week_4        472.4900  1424.4800   0.332   0.7486  
## Week_5      -1146.2948  1625.5386  -0.705   0.5007  
## Week_6        353.8557  2193.7364   0.161   0.8759  
## Week_7         16.4335  1789.1440   0.009   0.9929  
## Week_8      -1803.9206  2841.8755  -0.635   0.5433  
## Week_9       -480.1525  1559.8856  -0.308   0.7661  
## Week_10     -3795.2263  2475.9821  -1.533   0.1639  
## Week_11     -2046.9935  3834.3045  -0.534   0.6079  
## Week_12      1572.8965  2487.5815   0.632   0.5448  
## Week_13     -2297.9034  3903.8791  -0.589   0.5724  
## Week_14      3461.4017  5873.0915   0.589   0.5719  
## Week_15      6790.4379  3568.7313   1.903   0.0936 .
## Week_16      4154.3312  2238.2517   1.856   0.1005  
## Week_17      3397.8516  2451.7104   1.386   0.2032  
## Week_18     -1608.5163  2627.7381  -0.612   0.5574  
## Week_19       943.7657  2235.7629   0.422   0.6841  
## Week_20      1668.0157  2450.7102   0.681   0.5153  
## Week_21      -945.4573  2685.7118  -0.352   0.7339  
## Week_22         0.7152  2426.5036   0.000   0.9998  
## Week_23     -1077.3680  2381.9820  -0.452   0.6631  
## Week_24     -3291.8970  2083.1794  -1.580   0.1527  
## Week_25     -3647.3402  2741.2940  -1.331   0.2200  
## Week_26      4181.3355  2750.4315   1.520   0.1669  
## Week_27      2963.2074  3931.1454   0.754   0.4726  
## Week_28      1390.0122  3153.2425   0.441   0.6710  
## Week_29       400.7122  3202.7046   0.125   0.9035  
## Week_30     -1383.8310  2818.7972  -0.491   0.6367  
## Week_31     -3047.3280  2581.4560  -1.180   0.2717  
## Week_32       528.0668  1534.6270   0.344   0.7396  
## Week_33      -808.6604  2866.5675  -0.282   0.7850  
## Week_34      6494.5491  2946.6155   2.204   0.0586 .
## Week_35      1495.1868  2024.5950   0.739   0.4813  
## Week_36      -128.2266  2718.9666  -0.047   0.9635  
## Week_37     -5400.3101  3239.6164  -1.667   0.1341  
## Week_38      4013.8240  2923.6343   1.373   0.2070  
## Week_39     -6848.5002  2885.2634  -2.374   0.0450 *
## Week_40       350.0976  2530.5603   0.138   0.8934  
## Week_41     -2519.6301  2977.8319  -0.846   0.4221  
## Week_42      -677.9038  2969.9809  -0.228   0.8252  
## Week_43     -5285.3740  2318.6053  -2.280   0.0521 .
## Week_44      -552.8590  1725.5989  -0.320   0.7569  
## Week_45     -6062.1221  2580.1004  -2.350   0.0467 *
## Week_46      3191.7414  2406.0598   1.327   0.2213  
## Week_47      -176.6592  1466.2031  -0.120   0.9071  
## Week_48      1510.4028  2521.6136   0.599   0.5658  
## Week_49       743.4374  1325.2938   0.561   0.5902  
## Week_50      2045.2262  1394.0358   1.467   0.1805  
## Week_51     -1932.5901  1609.5402  -1.201   0.2642  
## Week_52      4028.7597  2215.8892   1.818   0.1066  
## Week_53     -2245.1934  1158.2854  -1.938   0.0886 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13480 on 8 degrees of freedom
## Multiple R-squared:  0.9119, Adjusted R-squared:  0.3282 
## F-statistic: 1.562 on 53 and 8 DF,  p-value: 0.2595

## [1] "Results for crop: Raspberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9762.3 -2100.8   230.9  2349.8  6195.7 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -61187.71   46168.70  -1.325   0.2217  
## Week_1        1141.71     825.08   1.384   0.2038  
## Week_2        2571.75     977.07   2.632   0.0301 *
## Week_3        -481.87    1056.24  -0.456   0.6604  
## Week_4        -107.62     879.49  -0.122   0.9056  
## Week_5        -212.14    1003.63  -0.211   0.8379  
## Week_6        -450.96    1354.44  -0.333   0.7477  
## Week_7        1304.39    1104.64   1.181   0.2716  
## Week_8        1800.95    1754.61   1.026   0.3347  
## Week_9       -1167.50     963.09  -1.212   0.2600  
## Week_10       1605.93    1528.70   1.051   0.3242  
## Week_11      -2342.20    2367.35  -0.989   0.3515  
## Week_12      -2007.14    1535.87  -1.307   0.2276  
## Week_13       1166.39    2410.31   0.484   0.6414  
## Week_14       4030.56    3626.13   1.112   0.2986  
## Week_15        193.04    2203.38   0.088   0.9323  
## Week_16       1030.72    1381.93   0.746   0.4771  
## Week_17        577.82    1513.72   0.382   0.7126  
## Week_18       1907.75    1622.40   1.176   0.2734  
## Week_19        513.95    1380.39   0.372   0.7193  
## Week_20      -1281.64    1513.10  -0.847   0.4216  
## Week_21       4363.44    1658.19   2.631   0.0301 *
## Week_22         41.49    1498.16   0.028   0.9786  
## Week_23      -2198.68    1470.67  -1.495   0.1733  
## Week_24       1860.18    1286.18   1.446   0.1861  
## Week_25        966.42    1692.51   0.571   0.5837  
## Week_26      -1731.03    1698.15  -1.019   0.3379  
## Week_27       2733.47    2427.14   1.126   0.2927  
## Week_28      -3773.68    1946.85  -1.938   0.0886 .
## Week_29        556.91    1977.39   0.282   0.7854  
## Week_30       1876.28    1740.36   1.078   0.3124  
## Week_31        369.58    1593.83   0.232   0.8225  
## Week_32       -600.33     947.50  -0.634   0.5440  
## Week_33      -2804.78    1769.86  -1.585   0.1517  
## Week_34       2074.56    1819.28   1.140   0.2871  
## Week_35      -2122.18    1250.01  -1.698   0.1280  
## Week_36       3432.87    1678.73   2.045   0.0751 .
## Week_37       1239.02    2000.18   0.619   0.5528  
## Week_38      -2025.65    1805.09  -1.122   0.2943  
## Week_39       -790.84    1781.40  -0.444   0.6688  
## Week_40       1590.43    1562.40   1.018   0.3385  
## Week_41       -368.28    1838.55  -0.200   0.8462  
## Week_42        451.98    1833.71   0.246   0.8115  
## Week_43       -110.83    1431.54  -0.077   0.9402  
## Week_44       -907.94    1065.41  -0.852   0.4189  
## Week_45       -646.03    1592.99  -0.406   0.6957  
## Week_46      -1443.39    1485.53  -0.972   0.3597  
## Week_47       -904.31     905.25  -0.999   0.3471  
## Week_48       1098.79    1556.88   0.706   0.5004  
## Week_49       1208.57     818.25   1.477   0.1779  
## Week_50      -1014.99     860.70  -1.179   0.2722  
## Week_51      -2542.57     993.75  -2.559   0.0337 *
## Week_52       2863.56    1368.12   2.093   0.0697 .
## Week_53      -1253.87     715.14  -1.753   0.1176  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8321 on 8 degrees of freedom
## Multiple R-squared:  0.9006, Adjusted R-squared:  0.242 
## F-statistic: 1.368 on 53 and 8 DF,  p-value: 0.3366

## [1] "Results for crop: Strawberries"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -8816  -2698    765   2981   9094 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -43671.26   62465.98  -0.699   0.5043  
## Week_1         102.83    1116.33   0.092   0.9289  
## Week_2          91.01    1321.97   0.069   0.9468  
## Week_3         977.08    1429.09   0.684   0.5135  
## Week_4         642.01    1189.95   0.540   0.6042  
## Week_5       -1503.25    1357.90  -1.107   0.3005  
## Week_6        1684.53    1832.55   0.919   0.3849  
## Week_7         663.88    1494.57   0.444   0.6687  
## Week_8       -2254.98    2373.98  -0.950   0.3700  
## Week_9        -233.27    1303.06  -0.179   0.8624  
## Week_10      -4874.81    2068.33  -2.357   0.0462 *
## Week_11      -3311.42    3203.01  -1.034   0.3314  
## Week_12       2091.01    2078.02   1.006   0.3438  
## Week_13        586.76    3261.13   0.180   0.8617  
## Week_14      -1237.21    4906.13  -0.252   0.8073  
## Week_15       5846.04    2981.16   1.961   0.0855 .
## Week_16       1960.95    1869.74   1.049   0.3249  
## Week_17       4846.62    2048.05   2.366   0.0455 *
## Week_18        -99.77    2195.10  -0.045   0.9649  
## Week_19       2500.27    1867.66   1.339   0.2175  
## Week_20       2429.33    2047.22   1.187   0.2694  
## Week_21      -1966.57    2243.53  -0.877   0.4063  
## Week_22       1595.38    2027.00   0.787   0.4539  
## Week_23      -1397.62    1989.80  -0.702   0.5024  
## Week_24      -3222.90    1740.20  -1.852   0.1012  
## Week_25      -1082.16    2289.96  -0.473   0.6491  
## Week_26       3451.49    2297.59   1.502   0.1714  
## Week_27       1871.65    3283.91   0.570   0.5844  
## Week_28       3016.32    2634.08   1.145   0.2853  
## Week_29      -4239.79    2675.40  -1.585   0.1517  
## Week_30       -730.59    2354.70  -0.310   0.7643  
## Week_31       1496.45    2156.44   0.694   0.5074  
## Week_32       -186.27    1281.96  -0.145   0.8881  
## Week_33      -2601.00    2394.61  -1.086   0.3090  
## Week_34       7501.91    2461.48   3.048   0.0159 *
## Week_35         71.91    1691.26   0.043   0.9671  
## Week_36      -1000.53    2271.31  -0.441   0.6712  
## Week_37      -2196.19    2706.24  -0.812   0.4405  
## Week_38       2494.36    2442.28   1.021   0.3370  
## Week_39      -5213.91    2410.22  -2.163   0.0625 .
## Week_40       3049.40    2113.92   1.443   0.1871  
## Week_41      -4397.99    2487.55  -1.768   0.1150  
## Week_42       1467.46    2480.99   0.591   0.5705  
## Week_43      -1320.41    1936.86  -0.682   0.5147  
## Week_44        -72.22    1441.49  -0.050   0.9613  
## Week_45      -6995.59    2155.30  -3.246   0.0118 *
## Week_46       4763.70    2009.92   2.370   0.0452 *
## Week_47        429.47    1224.80   0.351   0.7349  
## Week_48       -329.09    2106.45  -0.156   0.8797  
## Week_49        128.17    1107.09   0.116   0.9107  
## Week_50        -26.66    1164.52  -0.023   0.9823  
## Week_51       -454.82    1344.54  -0.338   0.7439  
## Week_52       2203.10    1851.06   1.190   0.2681  
## Week_53      -1115.07     967.58  -1.152   0.2824  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11260 on 8 degrees of freedom
## Multiple R-squared:  0.957,  Adjusted R-squared:  0.6718 
## F-statistic: 3.356 on 53 and 8 DF,  p-value: 0.03649

## # A tibble: 6 × 3
##   Crop_Type           Start_Year End_Year
##   <chr>                    <int>    <int>
## 1 Barley                    1991     2023
## 2 Canola                    1991     2023
## 3 Oats                      1991     2023
## 4 Peas, dry                 1991     2023
## 5 Rye, fall remaining       1991     2023
## 6 Wheat, spring             1991     2023

Part 4: new crop data

4.1:field crop data

yield

total production

total Cultivated area

4.2:fruits data

year range

## # A tibble: 39 × 4
## # Groups:   Estimates [3]
##    Estimates              Crop_Type                             Start_Year
##    <chr>                  <chr>                                      <int>
##  1 Marketed production    Fresh apples [114114111]                    1926
##  2 Marketed production    Fresh blueberries [1141114]                 1926
##  3 Marketed production    Fresh grapes [1141147]                      1926
##  4 Marketed production    Fresh peaches [114114411]                   1926
##  5 Marketed production    Fresh pears [114114211]                     1926
##  6 Marketed production    Fresh plums and prune plums [1141143]       1926
##  7 Marketed production    Fresh raspberries [114111211]               1926
##  8 Marketed production    Fresh strawberries [114111111]              1926
##  9 Cultivated area, total Fresh apples [114114111]                    2002
## 10 Cultivated area, total Fresh blueberries [1141114]                 2002
## 11 Cultivated area, total Fresh grapes [1141147]                      2002
## 12 Cultivated area, total Fresh nectarines [114114421]                2002
## 13 Cultivated area, total Fresh peaches [114114411]                   2002
## 14 Cultivated area, total Fresh pears [114114211]                     2002
## 15 Cultivated area, total Fresh plums and prune plums [1141143]       2002
## 16 Cultivated area, total Fresh raspberries [114111211]               2002
## 17 Cultivated area, total Fresh strawberries [114111111]              2002
## 18 Marketed production    Fresh nectarines [114114421]                2002
## 19 Cultivated area, total Fresh apricots [114114431]                  2007
## 20 Cultivated area, total Fresh cranberries [114111311]               2007
## 21 Cultivated area, total Fresh sour cherries [114114521]             2007
## 22 Cultivated area, total Fresh sweet cherries [114114511]            2007
## 23 Marketed production    Fresh apricots [114114431]                  2007
## 24 Marketed production    Fresh cranberries [114111311]               2007
## 25 Marketed production    Fresh sour cherries [114114521]             2007
## 26 Marketed production    Fresh sweet cherries [114114511]            2007
## 27 Total production       Fresh apples [114114111]                    2011
## 28 Total production       Fresh apricots [114114431]                  2011
## 29 Total production       Fresh blueberries [1141114]                 2011
## 30 Total production       Fresh cranberries [114111311]               2011
## 31 Total production       Fresh grapes [1141147]                      2011
## 32 Total production       Fresh nectarines [114114421]                2011
## 33 Total production       Fresh peaches [114114411]                   2011
## 34 Total production       Fresh pears [114114211]                     2011
## 35 Total production       Fresh plums and prune plums [1141143]       2011
## 36 Total production       Fresh raspberries [114111211]               2011
## 37 Total production       Fresh sour cherries [114114521]             2011
## 38 Total production       Fresh strawberries [114111111]              2011
## 39 Total production       Fresh sweet cherries [114114511]            2011
##    End_Year
##       <int>
##  1     2023
##  2     2023
##  3     2023
##  4     2023
##  5     2023
##  6     2023
##  7     2023
##  8     2023
##  9     2023
## 10     2023
## 11     2023
## 12     2023
## 13     2023
## 14     2023
## 15     2023
## 16     2023
## 17     2023
## 18     2023
## 19     2023
## 20     2023
## 21     2023
## 22     2023
## 23     2023
## 24     2023
## 25     2023
## 26     2023
## 27     2023
## 28     2023
## 29     2023
## 30     2023
## 31     2023
## 32     2023
## 33     2023
## 34     2023
## 35     2023
## 36     2023
## 37     2023
## 38     2023
## 39     2023

Yield (kilograms per hectare)

Marketed production (ton)

total Cultivated area (Hectares)

filter some fruits types

fill area with mean

mean as previous value

calculate the yield again

individual crop yield

4.3: lm with crop 1990-2024

FortStJohn monthly for Peace River region

## [1] "There are 5  NA in the matrix X in FortStJoh station"
## [1] "Results for crop: Barley"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -810.77 -210.13  -45.77  216.09  876.48 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2747.565    132.603  20.720 5.49e-15 ***
## Month_1        3.671      3.271   1.122  0.27511    
## Month_2       -1.625      5.889  -0.276  0.78550    
## Month_3      -10.737      6.665  -1.611  0.12288    
## Month_4      -21.387      5.908  -3.620  0.00171 ** 
## Month_5       27.866      8.546   3.261  0.00391 ** 
## Month_6       -5.543      4.113  -1.348  0.19282    
## Month_7      -16.804      9.772  -1.720  0.10094    
## Month_8      -15.634     20.745  -0.754  0.45987    
## Month_9      -34.634     18.477  -1.874  0.07555 .  
## Month_10     -46.651     33.615  -1.388  0.18047    
## Month_11      10.350      8.267   1.252  0.22500    
## Month_12       0.548      4.713   0.116  0.90859    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 442.6 on 20 degrees of freedom
## Multiple R-squared:  0.6662, Adjusted R-squared:  0.466 
## F-statistic: 3.327 on 12 and 20 DF,  p-value: 0.008594

## [1] "Results for crop: Canola"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -495.78 -201.82  -22.08  224.12  494.01 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.640e+03  1.049e+02  15.627 1.13e-12 ***
## Month_1      8.420e-01  2.588e+00   0.325  0.74832    
## Month_2     -3.685e-01  4.660e+00  -0.079  0.93776    
## Month_3     -2.422e+00  5.274e+00  -0.459  0.65100    
## Month_4      3.546e-01  4.674e+00   0.076  0.94028    
## Month_5      2.033e+01  6.762e+00   3.007  0.00697 ** 
## Month_6      4.823e-03  3.254e+00   0.001  0.99883    
## Month_7      1.363e+01  7.732e+00   1.763  0.09316 .  
## Month_8     -3.024e+01  1.641e+01  -1.842  0.08030 .  
## Month_9     -9.163e+00  1.462e+01  -0.627  0.53789    
## Month_10     9.810e+00  2.660e+01   0.369  0.71614    
## Month_11     2.620e+00  6.541e+00   0.401  0.69294    
## Month_12     2.417e+00  3.729e+00   0.648  0.52432    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350.2 on 20 degrees of freedom
## Multiple R-squared:  0.5118, Adjusted R-squared:  0.2189 
## F-statistic: 1.748 on 12 and 20 DF,  p-value: 0.1302

## [1] "Results for crop: Oats"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -686.30 -298.04   14.58  346.99  723.26 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2594.6436   152.9915  16.959 2.45e-13 ***
## Month_1        6.9024     3.7741   1.829  0.08236 .  
## Month_2       -6.6119     6.7945  -0.973  0.34211    
## Month_3        0.7560     7.6902   0.098  0.92267    
## Month_4      -13.0867     6.8161  -1.920  0.06924 .  
## Month_5       30.1494     9.8599   3.058  0.00621 ** 
## Month_6        2.8667     4.7455   0.604  0.55257    
## Month_7      -13.1842    11.2744  -1.169  0.25599    
## Month_8      -20.3265    23.9352  -0.849  0.40581    
## Month_9       -8.4361    21.3175  -0.396  0.69649    
## Month_10     -45.3332    38.7836  -1.169  0.25620    
## Month_11       0.5282     9.5375   0.055  0.95638    
## Month_12       6.9356     5.4377   1.275  0.21676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 510.6 on 20 degrees of freedom
## Multiple R-squared:  0.5482, Adjusted R-squared:  0.2772 
## F-statistic: 2.023 on 12 and 20 DF,  p-value: 0.07897

## [1] "Results for crop: Peas, dry"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -587.52 -153.37    4.09  172.14  555.40 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2283.3846   106.2762  21.485 2.74e-15 ***
## Month_1        0.4053     2.6217   0.155   0.8787    
## Month_2        4.8273     4.7198   1.023   0.3186    
## Month_3        7.8679     5.3420   1.473   0.1564    
## Month_4       -9.8101     4.7348  -2.072   0.0514 .  
## Month_5        9.9292     6.8493   1.450   0.1627    
## Month_6       -2.0050     3.2965  -0.608   0.5499    
## Month_7      -18.6597     7.8318  -2.383   0.0272 *  
## Month_8      -27.2808    16.6267  -1.641   0.1165    
## Month_9      -18.9174    14.8083  -1.277   0.2161    
## Month_10      22.9643    26.9412   0.852   0.4041    
## Month_11       5.6301     6.6252   0.850   0.4055    
## Month_12      -1.0693     3.7773  -0.283   0.7800    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 354.7 on 20 degrees of freedom
## Multiple R-squared:  0.632,  Adjusted R-squared:  0.4112 
## F-statistic: 2.863 on 12 and 20 DF,  p-value: 0.01824

## [1] "Results for crop: Rye, fall remaining"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -332.65 -101.24  -16.46  128.25  327.91 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2918.466     69.585  41.941  < 2e-16 ***
## Month_1       -5.645      1.717  -3.288  0.00367 ** 
## Month_2      -11.845      3.090  -3.833  0.00104 ** 
## Month_3       -8.053      3.498  -2.302  0.03219 *  
## Month_4       -2.919      3.100  -0.942  0.35765    
## Month_5        1.866      4.485   0.416  0.68170    
## Month_6       -4.689      2.158  -2.172  0.04202 *  
## Month_7        6.813      5.128   1.329  0.19895    
## Month_8      -12.300     10.886  -1.130  0.27190    
## Month_9        2.846      9.696   0.294  0.77216    
## Month_10     -21.121     17.640  -1.197  0.24516    
## Month_11      10.945      4.338   2.523  0.02021 *  
## Month_12       1.048      2.473   0.424  0.67638    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 232.3 on 20 degrees of freedom
## Multiple R-squared:  0.8199, Adjusted R-squared:  0.7118 
## F-statistic: 7.588 on 12 and 20 DF,  p-value: 4.424e-05

## [1] "Results for crop: Wheat, spring"
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -956.43 -258.19  -44.64  364.59  696.77 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2734.4157   165.1893  16.553 3.86e-13 ***
## Month_1       -0.1194     4.0750  -0.029  0.97692    
## Month_2       -5.4679     7.3362  -0.745  0.46474    
## Month_3        6.0417     8.3033   0.728  0.47528    
## Month_4      -22.9612     7.3595  -3.120  0.00540 ** 
## Month_5       30.8880    10.6461   2.901  0.00883 ** 
## Month_6       -1.6546     5.1238  -0.323  0.75011    
## Month_7      -29.6905    12.1732  -2.439  0.02417 *  
## Month_8       -5.4537    25.8436  -0.211  0.83500    
## Month_9       12.9619    23.0171   0.563  0.57960    
## Month_10     -42.4457    41.8757  -1.014  0.32287    
## Month_11     -12.2600    10.2979  -1.191  0.24777    
## Month_12       3.6215     5.8713   0.617  0.54431    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 551.4 on 20 degrees of freedom
## Multiple R-squared:  0.6203, Adjusted R-squared:  0.3925 
## F-statistic: 2.723 on 12 and 20 DF,  p-value: 0.02309

Part 5: veg data

## # A tibble: 322 × 4
## # Groups:   Crop_Type [34]
##    Crop_Type        Estimates                             Start_Year End_Year
##    <chr>            <chr>                                      <int>    <int>
##  1 Brussels sprouts Area harvested (acres)                      2007     2023
##  2 Brussels sprouts Area harvested (hectares)                   2007     2023
##  3 Brussels sprouts Area planted (acres)                        2007     2023
##  4 Brussels sprouts Area planted (hectares)                     2007     2023
##  5 Brussels sprouts Average yield per acre (pounds)             2007     2017
##  6 Brussels sprouts Average yield per hectare (kilograms)       2007     2017
##  7 Brussels sprouts Marketed production (metric tonnes)         2007     2023
##  8 Brussels sprouts Marketed production (tons)                  2007     2023
##  9 Brussels sprouts Total production (metric tonnes)            2007     2023
## 10 Brussels sprouts Total production (tons)                     2007     2023
## # ℹ 312 more rows
## # A tibble: 140 × 4
## # Groups:   Crop_Type [14]
##    Crop_Type Estimates                             Start_Year End_Year
##    <chr>     <chr>                                      <int>    <int>
##  1 asparagus Area harvested (acres)                      1982     2023
##  2 asparagus Area harvested (hectares)                   2002     2023
##  3 asparagus Area planted (acres)                        1940     2023
##  4 asparagus Area planted (hectares)                     2002     2023
##  5 asparagus Average yield per acre (pounds)             1940     2017
##  6 asparagus Average yield per hectare (kilograms)       2002     2017
##  7 asparagus Marketed production (metric tonnes)         2002     2023
##  8 asparagus Marketed production (tons)                  1982     2023
##  9 asparagus Total production (metric tonnes)            2002     2023
## 10 asparagus Total production (tons)                     1940     2023
## # ℹ 130 more rows

plot good quality replacement for yield data

plot ok quality replacement for yield data

plot soso quality replacement for yield data

plot mixed quality replacement for yield data

plot bad quality replacement for yield data